def load_model_and_dataset(dataset):
if dataset == 'mnist':
import mnist_NiN_bn
model = mnist_NiN_bn.NiN_Model()
saver = tf.train.Saver()
checkpoint = tf.train.latest_checkpoint(
'/home/bull/home/zmn/insight/sparse-imperceivable-attacks-master/models/mnist_NiN/'
)
saver.restore(sess, checkpoint)
data = MNIST()
elif dataset == "mnist2":
import mnist_model
model = mnist_model.MNISTModel()
data = MNIST()
elif dataset == 'cifar10':
import cifar_NiN_bn
model = cifar_NiN_bn.NiN_Model()
saver = tf.train.Saver()
checkpoint = tf.train.latest_checkpoint(
'/home/bull/home/zmn/insight/sparse-imperceivable-attacks-master/models/cifar_NiN/'
)
saver.restore(sess, checkpoint)
data = CIFAR()
else:
raise ValueError('unknown dataset')
return model, data
def main():
data, model = MNIST(), MNISTModel(restore="models/mnist", use_log=True)
origImgs, origLabels, origImgID = util.generate_attack_data_set(data, model, MGR)
delImgAT_Init = np.zeros(origImgs[0].shape)
objfunc = ObjectiveFunc.OBJFUNC(MGR, model, origImgs, origLabels)
MGR.Add_Parameter('eta', MGR.parSet['alpha']/origImgs[0].size)
MGR.Log_MetaData()
if(MGR.parSet['optimizer'] == 'ZOSVRG'):
delImgAT = svrg.ZOSVRG(delImgAT_Init, MGR, objfunc)
elif(MGR.parSet['optimizer'] == 'ZOSGD'):
delImgAT = sgd.ZOSGD(delImgAT_Init, MGR, objfunc)
else:
print('Please specify a valid optimizer')
for idx_ImgID in range(MGR.parSet['nFunc']):
currentID = origImgID[idx_ImgID]
orig_prob = model.model.predict(np.expand_dims(origImgs[idx_ImgID], axis=0))
advImg = np.tanh(np.arctanh(origImgs[idx_ImgID]*1.9999999)+delImgAT)/2.0
adv_prob = model.model.predict(np.expand_dims(advImg, axis=0))
suffix = "id{}_Orig{}_Adv{}".format(currentID, np.argmax(orig_prob), np.argmax(adv_prob))
util.save_img(advImg, "{}/Adv_{}.png".format(MGR.parSet['save_path'], suffix))
util.save_img(np.tanh(delImgAT)/2.0, "{}/Delta.png".format(MGR.parSet['save_path']))
sys.stdout.flush()
MGR.logHandler.close()
def main(_):
with tf.Session() as sess:
K.set_session(sess)
if FLAGS.dataset == 'MNIST':
data, model = MNIST(), MNISTModel("models/mnist", sess)
elif FLAGS.datset == 'Cifar':
data, model = CIFAR(), CIFARModel("models/cifar", sess)
def _model_fn(x, logits=False):
ybar, logits_ = model.predict(x)
if logits:
return ybar, logits_
return ybar
if FLAGS.dataset == 'MNIST':
x_adv = fgsm(_model_fn, x, epochs=9, eps=0.02)
elif FLAGS.datset == 'Cifar':
x_adv = fgsm(_model_fn, x, epochs=4, eps=0.01)
X_adv_test = attack(x_adv, data.test_data, data.test_labels, sess)
X_adv_train = attack(x_adv, data.train_data, data.train_labels, sess)
np.save('adversarial_outputs/fgsm_train_' + FLAGS.dataset.lower() + '.npy', X_adv_train)
np.save('adversarial_outputs/fgsm_test_' + FLAGS.dataset.lower() + '.npy', X_adv_test)
print("Legit/Adversarial training set")
model.evaluate(data.train_data, data.train_labels)
model.evaluate(X_adv_train, data.train_labels)
print("Legit/Adversarial test set")
model.evaluate(data.test_data, data.test_labels)
model.evaluate(X_adv_test, data.test_labels)
def compare_baseline():
data = MNIST()
model = MNISTModel("models/mnist")
sess = K.get_session()
attack = CarliniL2(sess, model, batch_size=100, max_iterations=3000,
binary_search_steps=4, targeted=False,
initial_const=10)
N = 100
test_adv = attack.attack(data.test_data[:N], data.test_labels[:N])
print('dist',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
def reconstruct(n_samples):
encoder = load_model('{}_encoder.h5'.format(desc))
decoder = load_model('{}_decoder.h5'.format(desc))
data = MNIST()
x_test = data.test_data
choice = np.random.choice(np.arange(n_samples))
original = x_test[choice].reshape(1, 784)
#normalize = colors.Normalize(0., 255.)
#original = normalize(original)
latent = encoder.predict(original)
reconstruction = decoder.predict(latent)
draw([{"title": "Original", "image": original}, {"title": "Reconstruction", "image": reconstruction}])
def main(args):
if not os.path.isdir('models'):
os.makedirs('models')
if args['dataset'] == "mnist" or args['dataset'] == "all":
train(MNIST(),
"models/mnist", [32, 32, 64, 64, 200, 200],
num_epochs=50)
if args['dataset'] == 'cifar' or args['dataset'] == 'all':
train(CIFAR(),
"models/cifar", [64, 64, 128, 128, 256, 256],
num_epochs=50)
def cw_attack(file_name, norm, sess, num_image=10, cifar = False, tinyimagenet = False):
np.random.seed(1215)
tf.set_random_seed(1215)
random.seed(1215)
if norm == '1':
attack = EADL1
norm_fn = lambda x: np.sum(np.abs(x),axis=(1,2,3))
elif norm == '2':
attack = CarliniL2
norm_fn = lambda x: np.sum(x**2,axis=(1,2,3))
elif norm == 'i':
attack = CarliniLi
norm_fn = lambda x: np.max(np.abs(x),axis=(1,2,3))
if cifar:
data = CIFAR()
elif tinyimagenet:
data = tinyImagenet()
else:
data = MNIST()
model = load_model(file_name, custom_objects={'fn':loss,'tf':tf, 'ResidualStart' : ResidualStart, 'ResidualStart2' : ResidualStart2})
inputs, targets, true_labels, true_ids, img_info = generate_data(data, samples=num_image, targeted=True, random_and_least_likely = True, target_type = 0b0010, predictor=model.predict, start=0)
model.predict = model
model.num_labels = 10
if cifar:
model.image_size = 32
model.num_channels = 3
elif tinyimagenet:
model.image_size = 64
model.num_channels = 3
model.num_labels = 200
else:
model.image_size = 28
model.num_channels = 1
start_time = timer.time()
attack = attack(sess, model, max_iterations = 1000)
perturbed_input = attack.attack(inputs, targets)
UB = np.average(norm_fn(perturbed_input-inputs))
return UB, (timer.time()-start_time)/len(inputs)
def main(args):
# load data
print("Loading data", args["dataset"])
if args["dataset"] == "mnist":
data = MNIST()
if args["train_data_source"]:
print("Using data from {}".format(args["train_data_source"]))
img = np.load("{}_data.npy".format(args["train_data_source"]))
labels = np.load("{}_data.npy".format(args["train_data_source"]))
data.validation_data = img
data.validation_labels = labels
elif args["dataset"] == "cifar10":
data = CIFAR()
elif args["dataset"] == "fe":
data = FACIAL()
elif args["dataset"] == "imagenet":
# use ImageDataGenerate provided by Keras
data = ImageNetDataGen(args["imagenet_train_dir"],
args["imagenet_validation_dir"],
data_augmentation=False)
print("Done...")
if args["dataset"] == "imagenet":
data_shape = (None, 299, 299, 3)
resize = 256
else:
data_shape = data.train_data.shape
resize = None
print("Start training autoencoder")
codec = CODEC(img_size=data_shape[1],
num_channels=data_shape[3],
compress_mode=args["compress_mode"],
resize=resize)
train_autoencoder(data,
codec,
batch_size=args["batch_size"],
epochs=args["epochs"],
saveFilePrefix=args["save_prefix"],
train_imagenet=(args["dataset"] == "imagenet"))
def plot(n_samples):
encoder = load_model('{}_encoder.h5'.format(desc))
data = MNIST()
x_test = data.test_data
y_test = data.test_labels
x = x_test[:n_samples].reshape(n_samples, 784)
y = y_test[:n_samples]
#normalize = colors.Normalize(0., 255.)
#x = normalize(x)
latent = encoder.predict(x)
if FLAGS.latent_dim > 2:
tsne = TSNE()
print("\nFitting t-SNE, this will take awhile...")
latent = tsne.fit_transform(latent)
fig, ax = plt.subplots()
for label in np.arange(10):
ax.scatter(latent[(y_test == label), 0], latent[(y_test == label), 1], label=label, s=3)
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
ax.set_aspect('equal')
ax.set_title("Latent Space")
plt.show(block=False)
raw_input("Press Enter to Exit")
def convert(file_name, new_name, cifar=False):
if not cifar:
eq_weights, new_params = get_weights(file_name)
data = MNIST()
else:
eq_weights, new_params = get_weights(file_name, inp_shape=(32, 32, 3))
data = CIFAR()
model = Sequential()
model.add(Flatten(input_shape=data.train_data.shape[1:]))
for param in new_params:
model.add(Dense(param))
model.add(Lambda(lambda x: tf.nn.relu(x)))
model.add(Dense(10))
for i in range(len(eq_weights)):
try:
print(eq_weights[i][0].shape)
except:
pass
model.layers[i].set_weights(eq_weights[i])
sgd = SGD(lr=0.01, decay=1e-5, momentum=0.9, nesterov=True)
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
model.save(new_name)
acc = model.evaluate(data.validation_data, data.validation_labels)[1]
printlog("Converting CNN to MLP")
nlayer = file_name.split('_')[-3][0]
filters = file_name.split('_')[-2]
kernel_size = file_name.split('_')[-1]
printlog(
"model name = {0}, numlayer = {1}, filters = {2}, kernel size = {3}".
format(file_name, nlayer, filters, kernel_size))
printlog("Model accuracy: {:.3f}".format(acc))
printlog("-----------------------------------")
return acc
def main(args):
with tf.Session() as sess:
random.seed(121)
np.random.seed(1211)
image_id = args['img_id']
arg_max_iter = args['maxiter']
arg_b = args['binary_steps']
arg_init_const = args['init_const']
arg_mode = args['mode']
arg_kappa = args['kappa']
arg_beta = args['beta']
arg_gamma = args['gamma']
AE_model = util.load_AE("mnist_AE_1")
data, model = MNIST(), MNISTModel("models/mnist", sess, False)
orig_prob, orig_class, orig_prob_str = util.model_prediction(
model, np.expand_dims(data.test_data[image_id], axis=0))
target_label = orig_class
print("Image:{}, infer label:{}".format(image_id, target_label))
orig_img, target = util.generate_data(data, image_id, target_label)
attack = AEADEN(sess,
model,
mode=arg_mode,
AE=AE_model,
batch_size=1,
kappa=arg_kappa,
init_learning_rate=1e-2,
binary_search_steps=arg_b,
max_iterations=arg_max_iter,
initial_const=arg_init_const,
beta=arg_beta,
gamma=arg_gamma)
adv_img = attack.attack(orig_img, target)
adv_prob, adv_class, adv_prob_str = util.model_prediction(
model, adv_img)
delta_prob, delta_class, delta_prob_str = util.model_prediction(
model, orig_img - adv_img)
INFO = "[INFO]id:{}, kappa:{}, Orig class:{}, Adv class:{}, Delta class: {}, Orig prob:{}, Adv prob:{}, Delta prob:{}".format(
image_id, arg_kappa, orig_class, adv_class, delta_class,
orig_prob_str, adv_prob_str, delta_prob_str)
print(INFO)
suffix = "id{}_kappa{}_Orig{}_Adv{}_Delta{}".format(
image_id, arg_kappa, orig_class, adv_class, delta_class)
arg_save_dir = "{}_ID{}_Gamma_{}".format(arg_mode, image_id, arg_gamma)
os.system("mkdir -p Results/{}".format(arg_save_dir))
util.save_img(
orig_img,
"Results/{}/Orig_original{}.png".format(arg_save_dir, orig_class))
util.save_img(adv_img,
"Results/{}/Adv_{}.png".format(arg_save_dir, suffix))
util.save_img(
np.absolute(orig_img - adv_img) - 0.5,
"Results/{}/Delta_{}.png".format(arg_save_dir, suffix))
sys.stdout.flush()
print("training")
print('=================')
# run training with given dataset, and print progress
history = model.fit(inputs,
labels,
batch_size=batch_size,
validation_data=(inputs, labels),
epochs=num_epochs,
shuffle=True)
# # save model to a file
# if file_name != None:
# model.save(file_name)
print('=================')
print('finished training')
print('==================')
return {'model': nlayer_model, 'history': None}
if not os.path.isdir('models'):
os.makedirs('models')
if __name__ == '__main__':
print(MNIST().train_data.shape[1:])
train(MNIST(),
file_name="models/mnist_5layer_relu",
params=[20, 20, 20, 20],
num_epochs=50,
lr=0.02,
decay=1e-4)
def main(args):
with tf.Session() as sess:
random.seed(SEED)
np.random.seed(SEED)
tf.set_random_seed(SEED)
image_id_set = np.random.choice(range(1000),
args["image_number"] * 3,
replace=False)
#image_id_set = np.random.randint(1, 1000, args["image_number"] )
arg_max_iter = args['maxiter'] ### max number of iterations
arg_init_const = args[
'init_const'] ### regularization prior to attack loss
arg_kappa = args['kappa'] ### attack confidence level
arg_q = args['q'] ### number of random direction vectors
arg_mode = args['mode'] ### algorithm name
arg_save_iteration = args['save_iteration']
arg_Dataset = args["dataset"]
arg_targeted_attack = args["targeted_attack"]
arg_bsz = args["mini_batch_sz"]
idx_lr = args["lr_idx"]
## load classofier For MNIST and CIFAR pixel value range is [-0.5,0.5]
if (arg_Dataset == 'mnist'):
data, model = MNIST(), MNISTModel("models/mnist", sess, True)
elif (arg_Dataset == 'cifar10'):
data, model = CIFAR(), CIFARModel("models/cifar", sess, True)
elif (arg_Dataset == 'imagenet'):
data, model = ImageNet(SEED), InceptionModel(sess, True)
else:
print('Please specify a valid dataset')
succ_count, ii, iii = 0, 0, 0
final_distortion_count,first_iteration_count, first_distortion_count = [], [], []
while iii < args["image_number"]:
ii = ii + 1
image_id = image_id_set[ii]
# if image_id!= 836: continue # for test only
orig_prob, orig_class, orig_prob_str = util.model_prediction(
model, np.expand_dims(data.test_data[image_id],
axis=0)) ## orig_class: predicted label;
if arg_targeted_attack: ### target attack
target_label = np.remainder(orig_class + 1, 10)
else:
target_label = orig_class
orig_img, target = util.generate_data(data, image_id, target_label)
# shape of orig_img is (1,28,28,1) in [-0.5, 0.5]
true_label_list = np.argmax(data.test_labels, axis=1)
true_label = true_label_list[image_id]
print("Image ID:{}, infer label:{}, true label:{}".format(
image_id, orig_class, true_label))
if true_label != orig_class:
print(
"True Label is different from the original prediction, pass!"
)
continue
else:
iii = iii + 1
print('\n', iii, '/', args["image_number"])
## parameter
d = orig_img.size # feature dim
print("dimension = ", d)
# mu=1/d**2 # smoothing parameter
q = arg_q + 0
I = arg_max_iter + 0
kappa = arg_kappa + 0
const = arg_init_const + 0
## flatten image to vec
orig_img_vec = np.resize(orig_img, (1, d))
delta_adv = np.zeros((1, d)) ### initialized adv. perturbation
#delta_adv = np.random.uniform(-16/255,16/255,(1,d))
## w adv image initialization
if args["constraint"] == 'uncons':
# * 0.999999 to avoid +-0.5 return +-infinity
w_ori_img_vec = np.arctanh(
2 * (orig_img_vec) * 0.999999
) # in real value, note that orig_img_vec in [-0.5, 0.5]
w_img_vec = np.arctanh(
2 * (np.clip(orig_img_vec + delta_adv, -0.5, 0.5)) *
0.999999)
else:
w_ori_img_vec = orig_img_vec.copy()
w_img_vec = np.clip(w_ori_img_vec + delta_adv, -0.5, 0.5)
# ## test ##
# for test_value in w_ori_img_vec[0, :]:
# if np.isnan(test_value) or np.isinf(test_value):
# print(test_value)
# initialize the best solution & best loss
best_adv_img = [] # successful adv image in [-0.5, 0.5]
best_delta = [] # best perturbation
best_distortion = (0.5 * d)**2 # threshold for best perturbation
total_loss = np.zeros(I) ## I: max iters
l2s_loss_all = np.zeros(I)
attack_flag = False
first_flag = True ## record first successful attack
# parameter setting for ZO gradient estimation
mu = args["mu"] ### smoothing parameter
## learning rate
base_lr = args["lr"]
if arg_mode == "ZOAdaMM":
## parameter initialization for AdaMM
v_init = 1e-7 #0.00001
v_hat = v_init * np.ones((1, d))
v = v_init * np.ones((1, d))
m = np.zeros((1, d))
# momentum parameter for first and second order moment
beta_1 = 0.9
beta_2 = 0.9 # only used by AMSGrad
print(beta_1, beta_2)
#for i in tqdm(range(I)):
for i in range(I):
if args["decay_lr"]:
base_lr = args["lr"] / np.sqrt(i + 1)
## Total loss evaluation
if args["constraint"] == 'uncons':
total_loss[i], l2s_loss_all[
i] = function_evaluation_uncons(
w_img_vec, kappa, target_label, const, model,
orig_img, arg_targeted_attack)
else:
total_loss[i], l2s_loss_all[i] = function_evaluation_cons(
w_img_vec, kappa, target_label, const, model, orig_img,
arg_targeted_attack)
## gradient estimation w.r.t. w_img_vec
if arg_mode == "ZOSCD":
grad_est = grad_coord_estimation(mu, q, w_img_vec, d,
kappa, target_label,
const, model, orig_img,
arg_targeted_attack,
args["constraint"])
elif arg_mode == "ZONES":
grad_est = gradient_estimation_NES(mu, q, w_img_vec, d,
kappa, target_label,
const, model, orig_img,
arg_targeted_attack,
args["constraint"])
else:
grad_est = gradient_estimation_v2(mu, q, w_img_vec, d,
kappa, target_label,
const, model, orig_img,
arg_targeted_attack,
args["constraint"])
# if np.remainder(i,50)==0:
# print("total loss:",total_loss[i])
# print(np.linalg.norm(grad_est, np.inf))
## ZO-Attack, unconstrained optimization formulation
if arg_mode == "ZOSGD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOsignSGD":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if arg_mode == "ZOSCD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOAdaMM":
m = beta_1 * m + (1 - beta_1) * grad_est
v = beta_2 * v + (1 - beta_2) * np.square(grad_est) ### vt
v_hat = np.maximum(v_hat, v)
#print(np.mean(v_hat))
delta_adv = delta_adv - base_lr * m / np.sqrt(v_hat)
if args["constraint"] == 'cons':
tmp = delta_adv.copy()
#X_temp = orig_img_vec.reshape((-1,1))
#V_temp2 = np.diag(np.sqrt(v_hat.reshape(-1)+1e-10))
V_temp = np.sqrt(v_hat.reshape(1, -1))
delta_adv = projection_box(tmp, orig_img_vec, V_temp,
-0.5, 0.5)
#delta_adv2 = projection_box_2(tmp, X_temp, V_temp2, -0.5, 0.5)
# v_init = 1e-2 #0.00001
# v = v_init * np.ones((1, d))
# m = np.zeros((1, d))
# # momentum parameter for first and second order moment
# beta_1 = 0.9
# beta_2 = 0.99 # only used by AMSGrad
# m = beta_1 * m + (1-beta_1) * grad_est
# v = np.maximum(beta_2 * v + (1-beta_2) * np.square(grad_est),v)
# delta_adv = delta_adv - base_lr * m /np.sqrt(v+1e-10)
# if args["constraint"] == 'cons':
# V_temp = np.diag(np.sqrt(v.reshape(-1)+1e-10))
# X_temp = orig_img_vec.reshape((-1,1))
# delta_adv = projection_box(delta_adv, X_temp, V_temp, -0.5, 0.5)
if arg_mode == "ZOSMD":
delta_adv = delta_adv - 0.5 * base_lr * grad_est
# delta_adv = delta_adv - base_lr* grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones_like(orig_img_vec)
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec,
V_temp, -0.5, 0.5)
if arg_mode == "ZOPSGD":
delta_adv = delta_adv - base_lr * grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones_like(orig_img_vec)
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec,
V_temp, -0.5, 0.5)
if arg_mode == "ZONES":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones_like(orig_img_vec)
#X = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec,
V_temp, -0.5, 0.5)
# if arg_mode == "ZO-AdaFom":
# m = beta_1 * m + (1-beta_1) * grad_est
# v = v* (float(i)/(i+1)) + np.square(grad_est)/(i+1)
# w_img_vec = w_img_vec - base_lr * m/np.sqrt(v)
##
### adv. example update
w_img_vec = w_ori_img_vec + delta_adv
## covert back to adv_img in [-0.5 , 0.5]
if args["constraint"] == 'uncons':
adv_img_vec = 0.5 * np.tanh((w_img_vec)) / 0.999999 #
else:
adv_img_vec = w_img_vec.copy()
adv_img = np.resize(adv_img_vec, orig_img.shape)
## update the best solution in the iterations
attack_prob, _, _ = util.model_prediction(model, adv_img)
target_prob = attack_prob[0, target_label]
attack_prob_tmp = attack_prob.copy()
attack_prob_tmp[0, target_label] = 0
other_prob = np.amax(attack_prob_tmp)
if args["print_iteration"]:
if np.remainder(i + 1, 1) == 0:
if true_label != np.argmax(attack_prob):
print(
"Iter %d (Succ): ID = %d, lr = %3.5f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %d"
% (i + 1, image_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob)))
else:
print(
"Iter %d (Fail): ID = %d, lr = %3.6f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %d"
% (i + 1, image_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob)))
if arg_save_iteration:
os.system("mkdir Examples")
if (np.logical_or(
true_label != np.argmax(attack_prob),
np.remainder(i + 1,
10) == 0)): ## every 10 iterations
suffix = "id_{}_Mode_{}_True_{}_Pred_{}_Ite_{}".format(
image_id, arg_mode, true_label,
np.argmax(attack_prob), i + 1)
# util.save_img(adv_img, "Examples/{}.png".format(suffix))
if arg_targeted_attack:
if (np.log(target_prob + 1e-10) -
np.log(other_prob + 1e-10) >=
kappa): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False ### once gets into this, it will no longer record the next sucessful attack
first_adv_img = adv_img
first_distortion = distortion(
adv_img, orig_img)
first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
else:
if (np.log(other_prob + 1e-10) -
np.log(target_prob + 1e-10) >=
kappa): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False
first_adv_img = adv_img
first_distortion = distortion(
adv_img, orig_img)
first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
if (attack_flag):
# os.system("mkdir Results_SL")
# ## best attack (final attack)
# suffix = "id_{}_Mode_{}_True_{}_Pred_{}".format(image_id, arg_mode, true_label, orig_class) ## orig_class, predicted label
# suffix2 = "id_{}_Mode_{}_True_{}_Pred_{}".format(image_id, arg_mode, true_label, adv_class)
# suffix3 = "id_{}_Mode_{}".format(image_id, arg_mode)
# ### save original image
# util.save_img(orig_img, "Results_SL/id_{}.png".format(image_id))
# util.save_img(orig_img, "Results_SL/{}_Orig.png".format(suffix))
# ### adv. image
# util.save_img(best_adv_img, "Results_SL/{}_Adv_best.png".format(suffix2))
# ### adv. perturbation
# util.save_img(best_delta, "Results_SL/{}_Delta_best.png".format(suffix3))
#
#
# ## first attack
# suffix4 = "id_{}_Mode_{}_True_{}_Pred_{}".format(image_id, arg_mode, true_label, first_class)
# ## first adv. imag
# util.save_img(first_adv_img, "Results_SL/{}_Adv_first.png".format(suffix4))
# ### first adv. perturbation
# util.save_img(first_delta, "Results_SL/{}_Delta_first.png".format(suffix3))
## save data
succ_count = succ_count + 1
final_distortion_count.append(l2s_loss_all[-1])
first_distortion_count.append(first_distortion)
first_iteration_count.append(first_iteration)
suffix0 = "retperimage2/id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_per".format(
image_id, arg_mode, args["constraint"], args["lr"],
int(args["decay_lr"]), args["exp_code"])
np.savez("{}".format(suffix0),
id=image_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
first_distortion=first_distortion,
first_iteration=first_iteration,
best_iteation=best_iteration,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
## print
print("It takes {} iteations to find the first attack".format(
first_iteration))
# print(total_loss)
else:
## save data
suffix0 = "retperimage2/id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_per".format(
image_id, arg_mode, args["constraint"], args["lr"],
int(args["decay_lr"]), args["exp_code"])
np.savez("{}".format(suffix0),
id=image_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
print("Attack Fails")
sys.stdout.flush()
print('succ rate:', succ_count / args["image_number"])
print('average first success l2', np.mean(first_distortion_count))
print('average first itrs', np.mean(first_iteration_count))
print('average l2:', np.mean(final_distortion_count), ' best l2:',
np.min(final_distortion_count), ' worst l2:',
np.max(final_distortion_count))
## contained in the LICENCE file in this directory.
from setup_mnist import MNIST
from mn_utils import prepare_data
from worker import AEDetector, SimpleReformer, IdReformer, AttackData, Classifier, Operator, Evaluator
import mn_utils as utils
detector_I = AEDetector("./defensive_models/MNIST_I", p=2)
detector_II = AEDetector("./defensive_models/MNIST_II", p=1)
reformer = SimpleReformer("./defensive_models/MNIST_I")
id_reformer = IdReformer()
classifier = Classifier("./models/example_classifier")
detector_dict = dict()
detector_dict["I"] = detector_I
detector_dict["II"] = detector_II
operator = Operator(MNIST(), classifier, detector_dict, reformer)
idx = utils.load_obj("example_idx")
_, _, Y = prepare_data(MNIST(), idx)
f = "example_carlini_0.0"
testAttack = AttackData(f, Y, "Carlini L2 0.0")
evaluator = Evaluator(operator, testAttack)
evaluator.plot_various_confidences("defense_performance",
drop_rate={"I": 0.001, "II": 0.001})
# run training with given dataset, and print progress
model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
return model
if not os.path.isdir('models'):
os.makedirs('models')
if __name__ == "__main__":
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-d', '--dataset', type=str, default="mnist")
args = vars(ap.parse_args())
if "mnist" in args["dataset"]:
MNIST()
if "cifar" in args["dataset"]:
CIFAR()
#train(MNIST(), file_name="models/mnist_2layer", params=[1024], num_epochs=1, lr=0.1, decay=1e-3)
#train(CIFAR(), file_name="models/cifar_2layer", params=[1024], num_epochs=1, lr=0.2, decay=1e-3)
r = np.random.random_integers(0,9)
l[i,r] = 1
return l
def attack(data, name):
sess = K.get_session()
model = load_model("models/"+name, custom_objects={'fn': fn})
class Wrap:
image_size = 28 if "mnist" in name else 32
num_labels = 10
num_channels = 1 if "mnist" in name else 3
def predict(self, x):
return model(x)
attack = CarliniL2(sess, Wrap(), batch_size=100,
max_iterations=10000, binary_search_steps=5,
initial_const=1, targeted=True)
adv = attack.attack(data.test_data[:100],
get_labs(data.test_labels[:100]))
np.save("/tmp/"+name, adv)
print(np.mean(np.sum((adv-data.test_data[:100])**2,axis=(1,2,3))**.5))
attack(MNIST(), "mnist")
attack(MNIST(), "mnist_brelu")
attack(MNIST(), "mnist_gaussian")
attack(MNIST(), "mnist_gaussian_brelu")
attack(CIFAR(), "cifar")
attack(CIFAR(), "cifar_brelu")
attack(CIFAR(), "cifar_gaussian")
attack(CIFAR(), "cifar_gaussian_brelu")
def expandImage(image_data):
image_data2 = np.array(image_data)
image_data2 = (image_data2 + 0.5) * 255
return image_data2
# In[4]:
if __name__ == "__main__":
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
modelPath = '%smodels/mnist' % (nn_robust_attack_root)
data, model = MNIST(), MNISTModel(modelPath, sess)
attack = CarliniLi(sess, model, max_iterations=1000, targeted=False)
inputs, targets = generate_data(data,
samples=1000,
targeted=False,
start=5500,
inception=False)
original_classified_wrong_number = 0 #number of benign samples that are misclassified
disturbed_failure_number = 0 #number of samples that failed to craft corresponding adversarial samples
test_number = 0 #number of adversarial samples that we generate
TTP = 0
TP = 0
FN = 0
help='number of epochs')
parser.add_argument('--overwrite',
action='store_true',
help='overwrite output file')
args = parser.parse_args()
print(args)
nlayers = len(args.layer_parameters) + 1
if not args.modelfile:
file_name = args.modelpath + "/" + args.model + "_" + str(
nlayers
) + "layer_" + args.activation + "_" + args.layer_parameters[0]
else:
file_name = args.modelfile
print("Model will be saved to", file_name)
if os.path.isfile(file_name) and not args.overwrite:
raise RuntimeError("model {} exists.".format(file_name))
if args.model == "mnist":
data = MNIST()
elif args.model == "cifar":
data = CIFAR()
train(data,
file_name=file_name,
params=args.layer_parameters,
num_epochs=args.epochs,
lr=args.lr,
decay=args.wd,
activation=args.activation,
activation_param=args.leaky_slope,
grad_reg=args.gradreg,
dropout_rate=args.dropout)
# now train the teacher at the given temperature
teacher = train(data, file_name+"_teacher", params, num_epochs, batch_size, train_temp,
init=file_name+"_init")
# evaluate the labels at temperature t
predicted = teacher.predict(data.train_data)
y = tf.nn.softmax(predicted/train_temp)
print(y)
data.train_labels = y
# train the student model at temperature t
student = train(data, file_name, params, num_epochs, batch_size, train_temp,
init=file_name+"_init")
# and finally we predict at temperature 1
predicted = student.predict(data.train_data)
print(predicted)
if not os.path.isdir('models'):
os.makedirs('models')
train(CIFAR(), "models/cifar", [64, 64, 128, 128, 256, 256], num_epochs=50)
train(MNIST(), "models/mnist", [32, 32, 64, 64, 200, 200], num_epochs=50)
train_distillation(MNIST(), "models/mnist-distilled-100", [32, 32, 64, 64, 200, 200],
num_epochs=50, train_temp=100)
train_distillation(CIFAR(), "models/cifar-distilled-100", [64, 64, 128, 128, 256, 256],
num_epochs=50, train_temp=100)
model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
return model
if not os.path.isdir('models'):
os.makedirs('models')
if __name__ == "__main__":
train(MNIST(),
file_name="models/mnist_2layer",
params=[1024],
num_epochs=50,
lr=0.1,
decay=1e-3)
train(CIFAR(),
file_name="models/cifar_2layer",
params=[1024],
num_epochs=50,
lr=0.2,
decay=1e-3)
num_epochs=100,
batch_size=256,
if_save=True):
self.model.compile(loss='mean_squared_error',
metrics=['mean_squared_error'],
optimizer='adam')
noise = self.v_noise * np.random.normal(size=np.shape(data.train_data))
noisy_train_data = data.train_data + noise
noisy_train_data = np.clip(noisy_train_data, -0.5, 0.5)
self.model.fit(noisy_train_data,
data.train_data,
batch_size=batch_size,
validation_data=(data.validation_data,
data.validation_data),
epochs=num_epochs,
shuffle=True)
if if_save:
self.model.save(os.path.join(self.model_dir, archive_name))
def load(self, archive_name, model_dir=None):
if model_dir is None: model_dir = self.model_dir
self.model.load_weights(os.path.join(model_dir, archive_name))
if __name__ == '__main__':
AE = DAE()
AE.train(MNIST(), "mnist")
def main(args):
with tf.Session() as sess:
print("Loading data and classification model: {}".format(
args["dataset"]))
if args['dataset'] == "mnist":
data, model = MNIST(), MNISTModel("models/mnist",
sess,
use_softmax=True)
elif args['dataset'] == "cifar10":
data, model = CIFAR(), CIFARModel("models/cifar",
sess,
use_softmax=True)
elif args['dataset'] == "imagenet":
# data, model = ImageNet(data_path=args["imagenet_dir"], targetFile=args["attack_single_img"]), InceptionModel(sess, use_softmax=True)
data, model = ImageNetDataNP(), InceptionModel(sess,
use_softmax=True)
# elif args['dataset'] == "imagenet_np":
if len(data.test_labels) < args["num_img"]:
raise Exception("No enough data, only have {} but need {}".format(
len(data.test_labels), args["num_img"]))
if args["attack_single_img"]:
# manually setup attack set
# attacking only one image with random attack]
orig_img = data.test_data
orig_labels = data.test_labels
orig_img_id = np.array([1])
if args["attack_type"] == "targeted":
target_labels = [
np.eye(model.num_labels)[args["single_img_target_label"]]
]
else:
target_labels = orig_labels
else:
# generate attack set
if args["dataset"] == "imagenet" or args[
"dataset"] == "imagenet_np":
shift_index = True
else:
shift_index = False
if args["random_target"] and (args["dataset"] == "imagenet"
or args["dataset"] == "imagenet_np"):
# find all possible class
all_class = np.unique(np.argmax(data.test_labels, 1))
all_orig_img, all_target_labels, all_orig_labels, all_orig_img_id = util.generate_attack_data_set(
data,
args["num_img"],
args["img_offset"],
model,
attack_type=args["attack_type"],
random_target_class=all_class,
shift_index=shift_index)
elif args["random_target"]:
# random target on all possible classes
class_num = data.test_labels.shape[1]
all_orig_img, all_target_labels, all_orig_labels, all_orig_img_id = util.generate_attack_data_set(
data,
args["num_img"],
args["img_offset"],
model,
attack_type=args["attack_type"],
random_target_class=list(range(class_num)),
shift_index=shift_index)
else:
all_orig_img, all_target_labels, all_orig_labels, all_orig_img_id = util.generate_attack_data_set(
data,
args["num_img"],
args["img_offset"],
model,
attack_type=args["attack_type"],
shift_index=shift_index)
# check attack data
# for i in range(len(orig_img_id)):
# tar_lab = np.argmax(target_labels[i])
# orig_lab = np.argmax(orig_labels[i])
# print("{}:, target label:{}, orig_label:{}, orig_img_id:{}".format(i, tar_lab, orig_lab, orig_img_id[i]))
# attack related settings
if args["attack_method"] == "zoo" or args[
"attack_method"] == "autozoom_bilin":
if args["img_resize"] is None:
args["img_resize"] = model.image_size
print(
"Argument img_resize is not set and not using autoencoder, set to image original size:{}"
.format(args["img_resize"]))
if args["attack_method"] == "zoo" or args["attack_method"] == "zoo_ae":
if args["batch_size"] is None:
args["batch_size"] = 128
print(
"Using zoo or zoo_ae attack, and batch_size is not set.\nSet batch_size to {}."
.format(args["batch_size"]))
else:
if args["batch_size"] is not None:
print("Argument batch_size is not used")
args["batch_size"] = 1 # force to be 1
if args["attack_method"] == "zoo_ae" or args[
"attack_method"] == "autozoom_ae":
#_, decoder = util.load_codec(args["codec_prefix"])
if args["dataset"] == "mnist" or args["dataset"] == "cifar10":
codec = CODEC(model.image_size,
model.num_channels,
args["compress_mode"],
use_tanh=False)
else:
codec = CODEC(128, model.num_channels, args["compress_mode"])
print(args["codec_prefix"])
codec.load_codec(args["codec_prefix"])
decoder = codec.decoder
print(decoder.input_shape)
args["img_resize"] = decoder.input_shape[1]
print("Using autoencoder, set the attack image size to:{}".format(
args["img_resize"]))
# setup attack
if args["attack_method"] == "zoo":
blackbox_attack = ZOO(sess, model, args)
elif args["attack_method"] == "zoo_ae":
blackbox_attack = ZOO_AE(sess, model, args, decoder)
elif args["attack_method"] == "autozoom_bilin":
blackbox_attack = AutoZOOM_BiLIN(sess, model, args)
elif args["attack_method"] == "autozoom_ae":
blackbox_attack = AutoZOOM_AE(sess, model, args, decoder, codec)
save_prefix = os.path.join(args["save_path"], args["dataset"],
args["attack_method"], args["attack_type"])
os.system("mkdir -p {}".format(save_prefix))
total_success = 0
l2_total = 0
for i in range(all_orig_img_id.size):
orig_img = all_orig_img[i:i + 1]
target = all_target_labels[i:i + 1]
label = all_orig_labels[i:i + 1]
target_class = np.argmax(target)
true_class = np.argmax(label)
test_index = all_orig_img_id[i]
# print information
print(
"[Info][Start]{}: test_index:{}, true label:{}, target label:{}"
.format(i, test_index, true_class, target_class))
if args["attack_method"] == "zoo_ae" or args[
"attack_method"] == "autozoom_ae":
#print ae info
if args["dataset"] == "mnist" or args["dataset"] == "cifar10":
temp_img = all_orig_img[i:i + 1]
else:
temp_img = all_orig_img[i]
temp_img = (temp_img + 0.5) * 255
temp_img = scipy.misc.imresize(temp_img, (128, 128))
temp_img = temp_img / 255 - 0.5
temp_img = np.expand_dims(temp_img, axis=0)
encode_img = codec.encoder.predict(temp_img)
decode_img = codec.decoder.predict(encode_img)
diff_img = (decode_img - temp_img)
diff_mse = np.mean(diff_img.reshape(-1)**2)
print("[Info][AE] MSE:{:.4f}".format(diff_mse))
timestart = time.time()
adv_img = blackbox_attack.attack(orig_img, target)
timeend = time.time()
if len(adv_img.shape) == 3:
adv_img = np.expand_dims(adv_img, axis=0)
l2_dist = np.sum((adv_img - orig_img)**2)**.5
adv_class = np.argmax(model.model.predict(adv_img))
success = False
if args["attack_type"] == "targeted":
if adv_class == target_class:
success = True
else:
if adv_class != true_class:
success = True
if success:
total_success += 1
l2_total += l2_dist
print(
"[Info][End]{}: test_index:{}, true label:{}, adv label:{}, success:{}, distortion:{:.5f}, success_rate:{:.4f}, l2_avg:{:.4f}"
.format(i, test_index, true_class, adv_class, success, l2_dist,
total_success / (i + 1),
0 if total_success == 0 else l2_total / total_success))
# save images
suffix = "id{}_testIndex{}_true{}_adv{}".format(
i, test_index, true_class, adv_class)
# original image
save_name = os.path.join(save_prefix, "Orig_{}.png".format(suffix))
util.save_img(orig_img, save_name)
save_name = os.path.join(save_prefix, "Orig_{}.npy".format(suffix))
np.save(save_name, orig_img)
# adv image
save_name = os.path.join(save_prefix, "Adv_{}.png".format(suffix))
util.save_img(adv_img, save_name)
save_name = os.path.join(save_prefix, "Adv_{}.npy".format(suffix))
np.save(save_name, adv_img)
# diff image
save_name = os.path.join(save_prefix, "Diff_{}.png".format(suffix))
util.save_img((adv_img - orig_img) / 2, save_name)
save_name = os.path.join(save_prefix, "Diff_{}.npy".format(suffix))
np.save(save_name, adv_img - orig_img)
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
return {'model':model, 'history':history}
if not os.path.isdir('models'):
os.makedirs('models')
if __name__ == '__main__':
train(MNIST(), file_name="models/mnist_resnet_2", nlayer=2, activation = tf.nn.relu)
train(MNIST(), file_name="models/mnist_resnet_3", nlayer=3, activation = tf.nn.relu)
train(MNIST(), file_name="models/mnist_resnet_4", nlayer=4, activation = tf.nn.relu)
train(MNIST(), file_name="models/mnist_resnet_5", nlayer=5, activation = tf.nn.relu)
train(MNIST(), file_name="models/mnist_resnet_2_sigmoid", nlayer=2, activation = tf.sigmoid)
train(MNIST(), file_name="models/mnist_resnet_3_sigmoid", nlayer=3, activation = tf.sigmoid)
train(MNIST(), file_name="models/mnist_resnet_4_sigmoid", nlayer=4, activation = tf.sigmoid)
train(MNIST(), file_name="models/mnist_resnet_5_sigmoid", nlayer=5, activation = tf.sigmoid)
train(MNIST(), file_name="models/mnist_resnet_2_tanh", nlayer=2, activation = tf.tanh)
train(MNIST(), file_name="models/mnist_resnet_3_tanh", nlayer=3, activation = tf.tanh)
train(MNIST(), file_name="models/mnist_resnet_4_tanh", nlayer=4, activation = tf.tanh)
train(MNIST(), file_name="models/mnist_resnet_5_tanh", nlayer=5, activation = tf.tanh)
train(MNIST(), file_name="models/mnist_resnet_2_atan", nlayer=2, activation = tf.atan)
def main(args):
with tf.Session() as sess:
if (args['dataset'] == 'mnist'):
data, model = MNIST(), MNISTModel("models/mnist", sess)
handpick = False
inception = False
if (args['dataset'] == "cifar"):
data, model = CIFAR(), CIFARModel("models/cifar", sess)
handpick = True
inception = False
if (args['dataset'] == "imagenet"):
data, model = ImageNet(args['seed_imagenet']), InceptionModel(sess)
handpick = True
inception = True
if (args['adversarial'] != "none"):
model = MNISTModel("models/mnist_cw" + str(args['adversarial']),
sess)
if (args['temp'] and args['dataset'] == 'mnist'):
model = MNISTModel("models/mnist-distilled-" + str(args['temp']),
sess)
if (args['temp'] and args['dataset'] == 'cifar'):
model = CIFARModel("models/cifar-distilled-" + str(args['temp']),
sess)
inputs, targets, labels, true_ids = generate_data(
data,
model,
samples=args['numimg'],
inception=inception,
handpick=handpick,
train=args['train'],
seed=args['seed'])
timestart = time.time()
if (args['attack'] == 'L2'):
attack = CarliniL2(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'L1'):
attack = EADL1(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'EN'):
attack = EADEN(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
"""If untargeted, pass labels instead of targets"""
if (args['attack'] == 'FGSM'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML1'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML2'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGSM'):
attack = IGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML1'):
attack = IGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML2'):
attack = IGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
inception=inception)
adv = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run",
len(inputs) / args['batch_size'], "random instances.")
if (args['train']):
np.save('labels_train.npy', labels)
np.save(str(args['attack']) + '_train.npy', adv)
return
r_best = []
d_best_l1 = []
d_best_l2 = []
d_best_linf = []
r_average = []
d_average_l1 = []
d_average_l2 = []
d_average_linf = []
r_worst = []
d_worst_l1 = []
d_worst_l2 = []
d_worst_linf = []
if (args['conf'] != 0):
model = MNISTModel("models/mnist-distilled-100", sess)
if (args['show']):
if not os.path.exists(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack'])):
os.makedirs(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']))
for i in range(0, len(inputs), args['batch_size']):
pred = []
for j in range(i, i + args['batch_size']):
if inception:
pred.append(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)))
else:
pred.append(model.model.predict(adv[j:j + 1]))
dist_l1 = 1e10
dist_l2 = 1e10
dist_linf = 1e10
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
for k, j in enumerate(range(i, i + args['batch_size'])):
if (np.argmax(pred[k], 1) == np.argmax(targets[j:j + 1], 1)):
if (np.sum(np.abs(adv[j] - inputs[j])) < dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) < dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) < dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_best_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_best_l1.append(
np.sum(np.abs(adv[dist_l1_index] - inputs[dist_l1_index])))
d_best_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_best.append(1)
else:
r_best.append(0)
rand_int = np.random.randint(i, i + args['batch_size'])
if inception:
pred_r = np.reshape(
model.model.predict(adv[rand_int:rand_int + 1]),
(data.test_labels[0:1].shape))
else:
pred_r = model.model.predict(adv[rand_int:rand_int + 1])
if (np.argmax(pred_r,
1) == np.argmax(targets[rand_int:rand_int + 1], 1)):
r_average.append(1)
d_average_l2.append(
np.sum((adv[rand_int] - inputs[rand_int])**2)**.5)
d_average_l1.append(
np.sum(np.abs(adv[rand_int] - inputs[rand_int])))
d_average_linf.append(
np.amax(np.abs(adv[rand_int] - inputs[rand_int])))
else:
r_average.append(0)
dist_l1 = 0
dist_l1_index = 1e10
dist_linf = 0
dist_linf_index = 1e10
dist_l2 = 0
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + args['batch_size'])):
if (np.argmax(pred[k], 1) != np.argmax(targets[j:j + 1], 1)):
r_worst.append(0)
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
break
else:
if (np.sum(np.abs(adv[j] - inputs[j])) > dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) > dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) > dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_worst_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_worst_l1.append(
np.sum(np.abs(adv[dist_l1_index] - inputs[dist_l1_index])))
d_worst_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_worst.append(1)
if (args['show']):
for j in range(i, i + args['batch_size']):
target_id = np.argmax(targets[j:j + 1], 1)
label_id = np.argmax(labels[j:j + 1], 1)
prev_id = np.argmax(
np.reshape(model.model.predict(inputs[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
adv_id = np.argmax(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
suffix = "id{}_seq{}_lbl{}_prev{}_adv{}_{}_l1_{:.3f}_l2_{:.3f}_linf_{:.3f}".format(
true_ids[i], target_id, label_id, prev_id,
adv_id, adv_id == target_id,
np.sum(np.abs(adv[j] - inputs[j])),
np.sum((adv[j] - inputs[j])**2)**.5,
np.amax(np.abs(adv[j] - inputs[j])))
show(
inputs[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']) +
"/original_{}.png".format(suffix))
show(
adv[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']) +
"/adversarial_{}.png".format(suffix))
print('best_case_L1_mean', np.mean(d_best_l1))
print('best_case_L2_mean', np.mean(d_best_l2))
print('best_case_Linf_mean', np.mean(d_best_linf))
print('best_case_prob', np.mean(r_best))
print('average_case_L1_mean', np.mean(d_average_l1))
print('average_case_L2_mean', np.mean(d_average_l2))
print('average_case_Linf_mean', np.mean(d_average_linf))
print('average_case_prob', np.mean(r_average))
print('worst_case_L1_mean', np.mean(d_worst_l1))
print('worst_case_L2_mean', np.mean(d_worst_l2))
print('worst_case_Linf_mean', np.mean(d_worst_linf))
print('worst_case_prob', np.mean(r_worst))
if init != None:
model.load_weights(init)
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted /
train_temp)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
nb_epoch=num_epochs,
shuffle=True)
if file_name != None:
model.save(file_name)
return model
if not os.path.isdir('models'):
os.makedirs('models')
#train(CIFAR(), "models/cifar", [64, 64, 128, 128, 256, 256], num_epochs=50)
train(MNIST(), "models/mnist", [32, 32, 64, 64, 200, 200], num_epochs=1)
def main(args):
with tf.Session() as sess:
if (args['dataset'] == 'mnist'):
data = MNIST()
inception = False
if (args['adversarial'] != "none"):
model = MNISTModel(
"models/mnist_cw" + str(args['adversarial']), sess)
elif (args['temp']):
model = MNISTModel(
"models/mnist-distilled-" + str(args['temp']), sess)
else:
model = MNISTModel("models/mnist", sess)
if (args['dataset'] == "cifar"):
data = CIFAR()
inception = False
if (args['adversarial'] != "none"):
model = CIFARModel(
"models/cifar_cw" + str(args['adversarial']), sess)
elif (args['temp']):
model = CIFARModel(
"models/cifar-distilled-" + str(args['temp']), sess)
else:
model = CIFARModel("models/cifar", sess)
if (args['dataset'] == "imagenet"):
data, model = ImageNet(args['seed_imagenet'],
2 * args['numimg']), InceptionModel(sess)
inception = True
inputs, targets, labels, true_ids = generate_data(
data,
model,
samples=args['numimg'],
targeted=not args['untargeted'],
target_num=args['targetnum'],
inception=inception,
train=args['train'],
seed=args['seed'])
timestart = time.time()
if (args['restore_np']):
if (args['train']):
adv = np.load(
str(args['dataset']) + '_' + str(args['attack']) +
'_train.npy')
else:
adv = np.load(
str(args['dataset']) + '_' + str(args['attack']) + '.npy')
else:
if (args['attack'] == 'L2'):
attack = CarliniL2(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'L1'):
attack = EADL1(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'EN'):
attack = EADEN(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
"""If untargeted, pass labels instead of targets"""
if (args['attack'] == 'FGSM'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML1'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML2'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGSM'):
attack = IFGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML1'):
attack = IFGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML2'):
attack = IFGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
timeend = time.time()
if args['untargeted']:
num_targets = 1
else:
num_targets = args['targetnum']
print("Took", timeend - timestart, "seconds to run",
len(inputs) / num_targets, "random instances.")
if (args['save_np']):
if (args['train']):
np.save(str(args['dataset']) + '_labels_train.npy', labels)
np.save(
str(args['dataset']) + '_' + str(args['attack']) +
'_train.npy', adv)
else:
np.save(
str(args['dataset']) + '_' + str(args['attack'] + '.npy'),
adv)
r_best_ = []
d_best_l1_ = []
d_best_l2_ = []
d_best_linf_ = []
r_average_ = []
d_average_l1_ = []
d_average_l2_ = []
d_average_linf_ = []
r_worst_ = []
d_worst_l1_ = []
d_worst_l2_ = []
d_worst_linf_ = []
#Transferability Tests
model_ = []
model_.append(model)
if (args['targetmodel'] != "same"):
if (args['targetmodel'] == "dd_100"):
model_.append(MNISTModel("models/mnist-distilled-100", sess))
num_models = len(model_)
if (args['show']):
if not os.path.exists(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack'])):
os.makedirs(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']))
for m, model in enumerate(model_):
r_best = []
d_best_l1 = []
d_best_l2 = []
d_best_linf = []
r_average = []
d_average_l1 = []
d_average_l2 = []
d_average_linf = []
r_worst = []
d_worst_l1 = []
d_worst_l2 = []
d_worst_linf = []
for i in range(0, len(inputs), num_targets):
pred = []
for j in range(i, i + num_targets):
if inception:
pred.append(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)))
else:
pred.append(model.model.predict(adv[j:j + 1]))
dist_l1 = 1e10
dist_l1_index = 1e10
dist_linf = 1e10
dist_linf_index = 1e10
dist_l2 = 1e10
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + num_targets)):
success = False
if (args['untargeted']):
if (np.argmax(pred[k], 1) != np.argmax(
targets[j:j + 1], 1)):
success = True
else:
if (np.argmax(pred[k],
1) == np.argmax(targets[j:j + 1], 1)):
success = True
if (success):
if (np.sum(np.abs(adv[j] - inputs[j])) < dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) < dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) < dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_best_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_best_l1.append(
np.sum(
np.abs(adv[dist_l1_index] -
inputs[dist_l1_index])))
d_best_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_best.append(1)
else:
r_best.append(0)
rand_int = np.random.randint(i, i + num_targets)
if inception:
pred_r = np.reshape(
model.model.predict(adv[rand_int:rand_int + 1]),
(data.test_labels[0:1].shape))
else:
pred_r = model.model.predict(adv[rand_int:rand_int + 1])
success_average = False
if (args['untargeted']):
if (np.argmax(pred_r, 1) != np.argmax(
targets[rand_int:rand_int + 1], 1)):
success_average = True
else:
if (np.argmax(pred_r, 1) == np.argmax(
targets[rand_int:rand_int + 1], 1)):
success_average = True
if success_average:
r_average.append(1)
d_average_l2.append(
np.sum((adv[rand_int] - inputs[rand_int])**2)**.5)
d_average_l1.append(
np.sum(np.abs(adv[rand_int] - inputs[rand_int])))
d_average_linf.append(
np.amax(np.abs(adv[rand_int] - inputs[rand_int])))
else:
r_average.append(0)
dist_l1 = 0
dist_l1_index = 1e10
dist_linf = 0
dist_linf_index = 1e10
dist_l2 = 0
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + num_targets)):
failure = True
if (args['untargeted']):
if (np.argmax(pred[k], 1) != np.argmax(
targets[j:j + 1], 1)):
failure = False
else:
if (np.argmax(pred[k],
1) == np.argmax(targets[j:j + 1], 1)):
failure = False
if failure:
r_worst.append(0)
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
break
else:
if (np.sum(np.abs(adv[j] - inputs[j])) > dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) > dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) > dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_worst_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_worst_l1.append(
np.sum(
np.abs(adv[dist_l1_index] -
inputs[dist_l1_index])))
d_worst_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_worst.append(1)
if (args['show'] and m == (num_models - 1)):
for j in range(i, i + num_targets):
target_id = np.argmax(targets[j:j + 1], 1)
label_id = np.argmax(labels[j:j + 1], 1)
prev_id = np.argmax(
np.reshape(model.model.predict(inputs[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
adv_id = np.argmax(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
suffix = "id{}_seq{}_lbl{}_prev{}_adv{}_{}_l1_{:.3f}_l2_{:.3f}_linf_{:.3f}".format(
true_ids[i], target_id, label_id, prev_id, adv_id,
adv_id == target_id,
np.sum(np.abs(adv[j] - inputs[j])),
np.sum((adv[j] - inputs[j])**2)**.5,
np.amax(np.abs(adv[j] - inputs[j])))
show(
inputs[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) +
"/" + str(args['attack']) +
"/original_{}.png".format(suffix))
show(
adv[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) +
"/" + str(args['attack']) +
"/adversarial_{}.png".format(suffix))
if (m != (num_models - 1)):
lbl = "Src_"
if (num_models > 2):
lbl += str(m) + "_"
else:
lbl = "Tgt_"
if (num_targets > 1):
print(lbl + 'best_case_L1_mean', np.mean(d_best_l1))
print(lbl + 'best_case_L2_mean', np.mean(d_best_l2))
print(lbl + 'best_case_Linf_mean', np.mean(d_best_linf))
print(lbl + 'best_case_prob', np.mean(r_best))
print(lbl + 'average_case_L1_mean', np.mean(d_average_l1))
print(lbl + 'average_case_L2_mean', np.mean(d_average_l2))
print(lbl + 'average_case_Linf_mean', np.mean(d_average_linf))
print(lbl + 'average_case_prob', np.mean(r_average))
print(lbl + 'worst_case_L1_mean', np.mean(d_worst_l1))
print(lbl + 'worst_case_L2_mean', np.mean(d_worst_l2))
print(lbl + 'worst_case_Linf_mean', np.mean(d_worst_linf))
print(lbl + 'worst_case_prob', np.mean(r_worst))
else:
print(lbl + 'L1_mean', np.mean(d_average_l1))
print(lbl + 'L2_mean', np.mean(d_average_l2))
print(lbl + 'Linf_mean', np.mean(d_average_linf))
print(lbl + 'success_prob', np.mean(r_average))
## Copyright (C) 2016, Nicholas Carlini <*****@*****.**>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
from setup_cifar import CIFAR, CIFARModel
from setup_mnist import MNIST, MNISTModel
from setup_inception import ImageNet, InceptionModel
import tensorflow as tf
import numpy as np
BATCH_SIZE = 1
with tf.Session() as sess:
data, model = MNIST(), MNISTModel("models/mnist", sess)
data, model = CIFAR(), CIFARModel("models/cifar", sess)
data, model = ImageNet(), InceptionModel(sess)
x = tf.placeholder(
tf.float32,
(None, model.image_size, model.image_size, model.num_channels))
y = model.predict(x)
r = []
for i in range(0, len(data.test_data), BATCH_SIZE):
pred = sess.run(y, {x: data.test_data[i:i + BATCH_SIZE]})
#print(pred)
#print('real',data.test_labels[i],'pred',np.argmax(pred))
r.append(
np.argmax(pred, 1) == np.argmax(data.test_labels[i:i +
def main(args):
temp_encoder = encoder(level=args['level'])
with tf.Session() as sess:
use_log = not args['use_zvalue']
is_inception = args['dataset'] == "imagenet"
# load network
print('Loading model', args['dataset'])
if args['dataset'] == "mnist":
data, model = MNIST(), MNISTModel("models/mnist", sess, use_log)
# data, model = MNIST(), MNISTModel("models/mnist-distilled-100", sess, use_log)
elif args['dataset'] == "cifar10":
#data, model = CIFAR(), CIFARModel("models/cifar", sess, use_log)
# data, model = CIFAR(), CIFARModel("models/cifar-distilled-100", sess, use_log)
data, model = CIFAR(), CIFAR_WIDE("models/wide_resnet", sess,
use_log)
elif args['dataset'] == "imagenet":
data, model = ImageNet(), InceptionModel(sess, use_log)
print('Done...')
if args['numimg'] == 0:
args['numimg'] = len(data.test_labels) - args['firstimg']
print('Using', args['numimg'], 'test images')
# load attack module
if args['attack'] == "white":
# batch size 1, optimize on 1 image at a time, rather than optimizing images jointly
attack = CarliniL2(sess,
model,
batch_size=1,
max_iterations=args['maxiter'],
print_every=args['print_every'],
early_stop_iters=args['early_stop_iters'],
confidence=0,
learning_rate=args['lr'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
use_log=use_log,
adam_beta1=args['adam_beta1'],
adam_beta2=args['adam_beta2'])
else:
# batch size 128, optimize on 128 coordinates of a single image
attack = BlackBoxL2(sess,
model,
batch_size=128,
max_iterations=args['maxiter'],
print_every=args['print_every'],
early_stop_iters=args['early_stop_iters'],
confidence=0,
learning_rate=args['lr'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
use_log=use_log,
use_tanh=args['use_tanh'],
use_resize=args['use_resize'],
adam_beta1=args['adam_beta1'],
adam_beta2=args['adam_beta2'],
reset_adam_after_found=args['reset_adam'],
solver=args['solver'],
save_ckpts=args['save_ckpts'],
load_checkpoint=args['load_ckpt'],
start_iter=args['start_iter'],
init_size=args['init_size'],
use_importance=not args['uniform'])
random.seed(args['seed'])
np.random.seed(args['seed'])
print('Generate data')
all_inputs, all_targets, all_labels, all_true_ids, encoding_all = generate_data(
data,
samples=args['numimg'],
targeted=not args['untargeted'],
start=args['firstimg'],
inception=is_inception)
print('Done...')
#print('all_inputs : ', all_inputs.shape)
#print('encoding_all : ',encoding_all.shape)
os.system("mkdir -p {}/{}".format(args['save'], args['dataset']))
img_no = 0
total_success = 0
l2_total = 0.0
origin_correct = 0
adv_correct = 0
for i in range(all_true_ids.size):
print(' adversarial_image_no: ', i)
inputs = all_inputs[i:i + 1]
encoding_inputs = encoding_all[i:i + 1]
#print('encoding_inputs shape: ', encoding_inputs)
targets = all_targets[i:i + 1]
labels = all_labels[i:i + 1]
print("true labels:", np.argmax(labels), labels)
print("target:", np.argmax(targets), targets)
# test if the image is correctly classified
original_predict = model.model.predict(encoding_inputs)
original_predict = np.squeeze(original_predict)
original_prob = np.sort(original_predict)
original_class = np.argsort(original_predict)
print("original probabilities:", original_prob[-1:-6:-1])
print("original classification:", original_class[-1:-6:-1])
print("original probabilities (most unlikely):", original_prob[:6])
print("original classification (most unlikely):",
original_class[:6])
if original_class[-1] != np.argmax(labels):
print(
"skip wrongly classified image no. {}, original class {}, classified as {}"
.format(i, np.argmax(labels), original_class[-1]))
continue
origin_correct += np.argmax(labels, 1) == original_class[-1]
img_no += 1
timestart = time.time()
adv, const = attack.attack_batch(inputs, targets)
if type(const) is list:
const = const[0]
if len(adv.shape) == 3:
adv = adv.reshape((1, ) + adv.shape)
timeend = time.time()
l2_distortion = np.sum((adv - inputs)**2)**.5
##### llj
encode_adv = np.transpose(adv, axes=(0, 3, 1, 2))
channel0, channel1, channel2 = encode_adv[:,
0, :, :], encode_adv[:,
1, :, :], encode_adv[:,
2, :, :]
channel0, channel1, channel2 = temp_encoder.tempencoding(
channel0), temp_encoder.tempencoding(
channel1), temp_encoder.tempencoding(channel2)
encode_adv = np.concatenate([channel0, channel1, channel2], axis=1)
encode_adv = np.transpose(encode_adv, axes=(0, 2, 3, 1))
#### llj
adversarial_predict = model.model.predict(encode_adv)
adversarial_predict = np.squeeze(adversarial_predict)
adversarial_prob = np.sort(adversarial_predict)
adversarial_class = np.argsort(adversarial_predict)
print("adversarial probabilities:", adversarial_prob[-1:-6:-1])
print("adversarial classification:", adversarial_class[-1:-6:-1])
adv_correct += np.argmax(labels, 1) == adversarial_class[-1]
success = False
if args['untargeted']:
if adversarial_class[-1] != original_class[-1]:
success = True
else:
if adversarial_class[-1] == np.argmax(targets):
success = True
if l2_distortion > 20.0:
success = False
if success:
total_success += 1
l2_total += l2_distortion
suffix = "id{}_seq{}_prev{}_adv{}_{}_dist{}".format(
all_true_ids[i], i, original_class[-1], adversarial_class[-1],
success, l2_distortion)
print("Saving to", suffix)
show(
inputs,
"{}/{}/{}_original_{}.png".format(args['save'],
args['dataset'], img_no,
suffix))
show(
adv,
"{}/{}/{}_adversarial_{}.png".format(args['save'],
args['dataset'], img_no,
suffix))
show(
adv - inputs,
"{}/{}/{}_diff_{}.png".format(args['save'], args['dataset'],
img_no, suffix))
print(
"[STATS][L1] total = {}, seq = {}, id = {}, time = {:.3f}, success = {}, const = {:.6f}, prev_class = {}, new_class = {}, distortion = {:.5f}, success_rate = {:.3f}, l2_avg = {:.5f}"
.format(img_no, i, all_true_ids[i], timeend - timestart,
success, const, original_class[-1],
adversarial_class[-1], l2_distortion,
total_success / float(img_no),
0 if total_success == 0 else l2_total / total_success))
sys.stdout.flush()
print(' origin accuracy : ',
100.0 * origin_correct / all_true_ids.size)
print(' adv accuracy : ', 100.0 * adv_correct / all_true_ids.size)
def main(args):
with tf.Session() as sess:
random.seed(SEED)
np.random.seed(SEED)
tf.set_random_seed(SEED)
class_id = args['class_id'] ### input image (natural example)
target_id = args[
'target_id'] ### target images id (adv example) if target attack
arg_max_iter = args['maxiter'] ### max number of iterations
arg_init_const = args[
'init_const'] ### regularization prior to attack loss
arg_kappa = args['kappa'] ### attack confidence level
arg_q = args['q'] ### number of random direction vectors
arg_mode = args['mode'] ### algorithm name
arg_save_iteration = args['save_iteration']
arg_Dataset = args["dataset"]
arg_targeted_attack = args["targeted_attack"]
arg_bsz = args["mini_batch_sz"]
idx_lr = args["lr_idx"]
## load classofier For MNIST and CIFAR pixel value range is [-0.5,0.5]
if (arg_Dataset == 'mnist'):
data, model = MNIST(), MNISTModel("models/mnist", sess, True)
elif (arg_Dataset == 'cifar10'):
data, model = CIFAR(), CIFARModel("models/cifar", sess, True)
elif (arg_Dataset == 'imagenet'):
data, model = ImageNet_Universal(SEED), InceptionModel(sess, True)
#model = InceptionModel(sess, True)
else:
print('Please specify a valid dataset')
#orig_img = np.load('ori_img_backup.npy')
orig_img = data.test_data[np.where(
np.argmax(data.test_labels, 1) == class_id)]
#np.save('ori_img_backup',orig_img)
#true_label = data.test_labels[np.where(np.argmax(data.test_labels,1) == class_id)]
_, orig_class = util.model_prediction_u(
model, orig_img[:30]
) # take 30 or less images to make sure arg_bsz number of them are valid
# filter out the images which misclassified already
orig_img = orig_img[np.where(orig_class == class_id)]
if orig_img.shape[0] < arg_bsz:
assert 'no enough valid inputs'
orig_img = orig_img[:arg_bsz]
np.save('original_imgsID' + str(class_id), orig_img)
#true_label = np.zeros((arg_bsz, 1001))
#true_label[np.arange(arg_bsz), class_id] = 1
true_label = class_id
if arg_targeted_attack: ### target attack
#target_label = np.zeros((arg_bsz, 1001))
#target_label[np.arange(arg_bsz), target_id] = 1
target_label = target_id
else:
target_label = true_label
#orig_img, target = util.generate_data(data, class_id, target_label)
# shape of orig_img is (1,28,28,1) in [-0.5, 0.5]
## parameter
if orig_img.ndim == 3 or orig_img.shape[0] == 1:
d = orig_img.size # feature dim
else:
d = orig_img[0].size
print("dimension = ", d)
# mu=1/d**2 # smoothing parameter
q = arg_q + 0
I = arg_max_iter + 0
kappa = arg_kappa + 0
const = arg_init_const + 0
## flatten image to vec
orig_img_vec = np.resize(orig_img, (arg_bsz, d))
## w adv image initialization
if args["constraint"] == 'uncons':
# * 0.999999 to avoid +-0.5 return +-infinity
w_ori_img_vec = np.arctanh(
2 * (orig_img_vec) * 0.999999
) # in real value, note that orig_img_vec in [-0.5, 0.5]
w_img_vec = w_ori_img_vec.copy()
else:
w_ori_img_vec = orig_img_vec.copy()
w_img_vec = w_ori_img_vec.copy()
# ## test ##
# for test_value in w_ori_img_vec[0, :]:
# if np.isnan(test_value) or np.isinf(test_value):
# print(test_value)
delta_adv = np.zeros((1, d)) ### initialized adv. perturbation
# initialize the best solution & best loss
best_adv_img = [] # successful adv image in [-0.5, 0.5]
best_delta = [] # best perturbation
best_distortion = (0.5 * d)**2 # threshold for best perturbation
total_loss = np.zeros(I) ## I: max iters
l2s_loss_all = np.zeros(I)
attack_flag = False
first_flag = True ## record first successful attack
# parameter setting for ZO gradient estimation
mu = args["mu"] ### smoothing parameter
## learning rate
base_lr = args["lr"]
if arg_mode == "ZOAdaMM":
## parameter initialization for AdaMM
v_init = 1e-7 #0.00001
v_hat = v_init * np.ones((1, d))
v = v_init * np.ones((1, d))
m = np.zeros((1, d))
# momentum parameter for first and second order moment
beta_1 = 0.9
beta_2 = 0.3 # only used by AMSGrad
print(beta_1, beta_2)
#for i in tqdm(range(I)):
for i in range(I):
if args["decay_lr"]:
base_lr = args["lr"] / np.sqrt(i + 1)
## Total loss evaluation
if args["constraint"] == 'uncons':
total_loss[i], l2s_loss_all[i] = function_evaluation_uncons(
w_img_vec, kappa, target_label, const, model, orig_img,
arg_targeted_attack)
else:
total_loss[i], l2s_loss_all[i] = function_evaluation_cons(
w_img_vec, kappa, target_label, const, model, orig_img,
arg_targeted_attack)
## gradient estimation w.r.t. w_img_vec
if arg_mode == "ZOSCD":
grad_est = grad_coord_estimation(mu, q, w_img_vec, d, kappa,
target_label, const, model,
orig_img, arg_targeted_attack,
args["constraint"])
elif arg_mode == "ZONES":
grad_est = gradient_estimation_NES(mu, q, w_img_vec, d, kappa,
target_label, const, model,
orig_img,
arg_targeted_attack,
args["constraint"])
else:
grad_est = gradient_estimation_v2(mu, q, w_img_vec, d, kappa,
target_label, const, model,
orig_img,
arg_targeted_attack,
args["constraint"])
# if np.remainder(i,50)==0:
# print("total loss:",total_loss[i])
# print(np.linalg.norm(grad_est, np.inf))
## ZO-Attack, unconstrained optimization formulation
if arg_mode == "ZOSGD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOsignSGD":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if arg_mode == "ZOSCD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOAdaMM":
m = beta_1 * m + (1 - beta_1) * grad_est
v = beta_2 * v + (1 - beta_2) * np.square(grad_est) ### vt
#print(np.mean(np.abs(m)),np.mean(np.sqrt(v)))
v_hat = np.maximum(v_hat, v)
delta_adv = delta_adv - base_lr * m / np.sqrt(v)
if args["constraint"] == 'cons':
tmp = delta_adv.copy()
#X_temp = orig_img_vec.reshape((-1,1))
#V_temp2 = np.diag(np.sqrt(v_hat.reshape(-1)+1e-10))
V_temp = np.sqrt(v_hat.reshape(1, -1))
delta_adv = projection_box(tmp, orig_img_vec, V_temp, -0.5,
0.5)
#delta_adv2 = projection_box_2(tmp, X_temp, V_temp2, -0.5, 0.5)
# v_init = 1e-2 #0.00001
# v = v_init * np.ones((1, d))
# m = np.zeros((1, d))
# # momentum parameter for first and second order moment
# beta_1 = 0.9
# beta_2 = 0.99 # only used by AMSGrad
# m = beta_1 * m + (1-beta_1) * grad_est
# v = np.maximum(beta_2 * v + (1-beta_2) * np.square(grad_est),v)
# delta_adv = delta_adv - base_lr * m /np.sqrt(v+1e-10)
# if args["constraint"] == 'cons':
# V_temp = np.diag(np.sqrt(v.reshape(-1)+1e-10))
# X_temp = orig_img_vec.reshape((-1,1))
# delta_adv = projection_box(delta_adv, X_temp, V_temp, -0.5, 0.5)
if arg_mode == "ZOSMD":
delta_adv = delta_adv - 0.5 * base_lr * grad_est
# delta_adv = delta_adv - base_lr* grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones((1, d))
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec, V_temp,
-0.5, 0.5)
if arg_mode == "ZOPSGD":
delta_adv = delta_adv - base_lr * grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones((1, d))
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec, V_temp,
-0.5, 0.5)
if arg_mode == "ZONES":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones((1, d))
#X = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec, V_temp,
-0.5, 0.5)
# if arg_mode == "ZO-AdaFom":
# m = beta_1 * m + (1-beta_1) * grad_est
# v = v* (float(i)/(i+1)) + np.square(grad_est)/(i+1)
# w_img_vec = w_img_vec - base_lr * m/np.sqrt(v)
##
### adv. example update
w_img_vec = w_ori_img_vec + delta_adv
## covert back to adv_img in [-0.5 , 0.5]
if args["constraint"] == 'uncons':
adv_img_vec = 0.5 * np.tanh((w_img_vec)) / 0.999999 #
else:
adv_img_vec = w_img_vec.copy()
adv_img = np.resize(adv_img_vec, orig_img.shape)
## update the best solution in the iterations
attack_prob, _, _ = util.model_prediction(model, adv_img)
target_prob = attack_prob[:, target_label]
attack_prob_tmp = attack_prob.copy()
attack_prob_tmp[:, target_label] = 0
other_prob = np.amax(attack_prob_tmp, 1)
if i % 1000 == 0 and i != 0:
if arg_mode == "ZOAdaMM": print(beta_1, beta_2)
print("save delta_adv")
np.save(
'retimgs/' + str(i) + 'itrs' +
str(np.argmax(attack_prob, 1)) + arg_mode +
str(args["lr"]), delta_adv)
if args["print_iteration"]:
if np.remainder(i + 1, 20) == 0:
if (true_label != np.argmax(attack_prob, 1)).all():
print(
"Iter %d (Succ): ID = %d, lr = %3.7f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %s"
% (i + 1, class_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob, 1)))
else:
sr = np.sum(
true_label != np.argmax(attack_prob, 1)) / arg_bsz
print(
"Iter %d (Fail): ID = %d, lr = %3.7f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %s, succ rate = %.2f"
% (i + 1, class_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob, 1), sr))
if arg_save_iteration:
os.system("mkdir Examples")
if (np.logical_or(
true_label != np.argmax(attack_prob, 1),
np.remainder(i + 1, 10) == 0)): ## every 10 iterations
suffix = "id_{}_Mode_{}_True_{}_Pred_{}_Ite_{}".format(
class_id, arg_mode, true_label,
np.argmax(attack_prob, 1), i + 1)
# util.save_img(adv_img, "Examples/{}.png".format(suffix))
if arg_targeted_attack:
if ((np.log(target_prob + 1e-10) - np.log(other_prob + 1e-10))
>= kappa).all(): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
#best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob, 1)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False ### once gets into this, it will no longer record the next sucessful attack
first_adv_img = adv_img
first_distortion = distortion(adv_img, orig_img)
#first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
else:
if ((np.log(other_prob + 1e-10) - np.log(target_prob + 1e-10))
>= kappa).all(): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
#best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob, 1)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False
first_adv_img = adv_img
first_distortion = distortion(adv_img, orig_img)
#first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
if (attack_flag):
# os.system("mkdir Results_SL")
# ## best attack (final attack)
# suffix = "id_{}_Mode_{}_True_{}_Pred_{}".format(class_id, arg_mode, true_label, orig_class) ## orig_class, predicted label
# suffix2 = "id_{}_Mode_{}_True_{}_Pred_{}".format(class_id, arg_mode, true_label, adv_class)
# suffix3 = "id_{}_Mode_{}".format(class_id, arg_mode)
# ### save original image
# util.save_img(orig_img, "Results_SL/id_{}.png".format(class_id))
# util.save_img(orig_img, "Results_SL/{}_Orig.png".format(suffix))
# ### adv. image
# util.save_img(best_adv_img, "Results_SL/{}_Adv_best.png".format(suffix2))
# ### adv. perturbation
# util.save_img(best_delta, "Results_SL/{}_Delta_best.png".format(suffix3))
#
#
# ## first attack
# suffix4 = "id_{}_Mode_{}_True_{}_Pred_{}".format(class_id, arg_mode, true_label, first_class)
# ## first adv. imag
# util.save_img(first_adv_img, "Results_SL/{}_Adv_first.png".format(suffix4))
# ### first adv. perturbation
# util.save_img(first_delta, "Results_SL/{}_Delta_first.png".format(suffix3))
## save data
suffix0 = "id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_ini_{}".format(
class_id, arg_mode, args["constraint"], str(args["lr"]),
int(args["decay_lr"]), args["exp_code"], args["init_const"])
np.savez("{}".format(suffix0),
id=class_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
first_distortion=first_distortion,
first_iteration=first_iteration,
best_iteation=best_iteration,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
## print
print("It takes {} iteations to find the first attack".format(
first_iteration))
# print(total_loss)
else:
## save data
suffix0 = "id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_ini_{}".format(
class_id, arg_mode, args["constraint"], str(args["lr"]),
int(args["decay_lr"]), args["exp_code"], args["init_const"])
np.savez("{}".format(suffix0),
id=class_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
print("Attack Fails")
sys.stdout.flush()
def run(args, restrict=True):
if restrict:
# Restrict the visible GPUs to the one for this subprocess
id = np.int(multiprocessing.current_process().name.split("-")[1])
os.environ["CUDA_VISIBLE_DEVICES"] = str(id - 1)
# Load Parameters
dataset = args[0]
epsilon = float(args[1])
mode = args[2]
K = int(args[3])
fname = dataset + "/" + str(epsilon) + "_" + mode + "_" + str(K)
# Configure Keras/Tensorflow
Keras.clear_session()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
sess = Keras.get_session()
Keras.set_learning_phase(False)
# Fix Random Seeds
np.random.seed(1)
tf.set_random_seed(
1
) #Having this before keras.clear_session() causes it it hang for some reason
# Load Model/Data and setup SPSA placeholders
N = 50
if dataset == "MNIST":
# Base Model
base_model = MNISTModel("../1-Models/MNIST")
data = MNIST()
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
# SPSA
shape_spsa = (1, 28, 28, 1)
x_spsa = tf.placeholder(tf.float32, shape=shape_spsa)
elif dataset == "CIFAR":
# Base Model
base_model = CIFARModel("../1-Models/CIFAR")
data = CIFAR()
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
# SPSA
shape_spsa = (1, 32, 32, 3)
x_spsa = tf.placeholder(tf.float32, shape=shape_spsa)
y_spsa = tf.placeholder(tf.int32)
# Load the hidden representations of the real and adversarial examples from the training set
x_train_real = np.squeeze(
np.load("../3-Representation/" + dataset + "/train_" + mode + ".npy"))
x_train_adv = np.squeeze(
np.load("../3-Representation/" + dataset + "/train_adv_" + mode +
".npy"))
n_train = x_train_real.shape[0]
n_train_adv = x_train_adv.shape[0]
x_train = np.float32(np.vstack((x_train_real, x_train_adv)))
#print("Bounds ", np.max(np.abs(x_train)))
y_train = np.float32(
np.hstack((-1.0 * np.ones(n_train), np.ones(n_train_adv))))
# Create the defended model
model_defended = DefendedModel(base_model, x_train, y_train, K)
defended_logits = model_defended.get_logits(x)
# Configure the attack
attack = SPSA(model_defended, back="tf", sess=sess)
with tf.name_scope("Attack") as scope:
gen = attack.generate(x_spsa,
y=y_spsa,
epsilon=epsilon,
is_targeted=False,
num_steps=100,
batch_size=2048,
early_stop_loss_threshold=-5.0)
# Run the attack
f = open(fname + ".txt", "w")
sample = np.random.choice(data.test_data.shape[0], N, replace=False)
x_sample = data.test_data[sample]
y_sample = np.argmax(data.test_labels[sample], axis=1)
logits_nat = sess.run(defended_logits, {x: x_sample})
f.write("Accuracy on Natural Images: " +
str(np.mean(np.argmax(logits_nat, axis=1) == y_sample)) + "\n")
pred_adv = -1.0 * np.ones((N))
for i in range(N):
x_real = x_sample[i].reshape(shape_spsa)
x_adv = sess.run(gen, {x_spsa: x_real, y_spsa: y_sample[i]})
pred_adv[i] = np.argmax(sess.run(defended_logits, {x: x_adv}))
f.write("Accuracy on Adversarial Images: " +
str(np.mean(pred_adv == y_sample)))
f.close()
