def generate_carlini_l2_examples(sess, model, x, y, X, Y, attack_params,
verbose, attack_log_fpath):
model_wrapper = wrap_to_tohinz_model(model, X, Y)
accepted_params = [
'batch_size', 'confidence', 'targeted', 'learning_rate',
'binary_search_steps', 'max_iterations', 'abort_early', 'initial_const'
]
for k in attack_params:
if k not in accepted_params:
raise NotImplementedError("Unsuporrted params in Carlini L2: %s" %
k)
# assert batch_size <= len(X)
if 'batch_size' in attack_params and attack_params['batch_size'] > len(X):
attack_params['batch_size'] = len(X)
if 'binary_search_steps' in attack_params:
attack_params['binary_search_steps'] = int(
attack_params['binary_search_steps'])
attack = CarliniL2(sess, model_wrapper, **attack_params)
if not verbose:
disablePrint(attack_log_fpath)
# The input range is [0, 1], convert to [-0.5, 0.5] by subtracting 0.5.
# The return range is [-0.5, 0.5]. Convert back to [0,1] by adding 0.5.
X_adv = attack.attack(X - 0.5, Y) + 0.5
if not verbose:
enablePrint()
return X_adv
def run_pca(Data, num_components=10, invert=False):
data = Data()
sess = K.get_session()
K.set_learning_phase(False)
shape = (-1, 784)
pca = sklearn.decomposition.PCA(n_components=num_components)
pca.fit(data.train_data.reshape(shape)) # [:10000]
if invert:
model = MNISTModel("models/mnist-pca-cnn-top-"+str(num_components))
else:
model = make_model(num_components)
model.load_weights("models/mnist-pca-top-"+str(num_components))
model = Wrap(model,pca)
tf_mean = tf.constant(pca.mean_,dtype=tf.float32)
tf_components = tf.constant(pca.components_.T,dtype=tf.float32)
def new_predict(xs):
# map to PCA space
xs = tf.reshape(xs,(-1,784))
xs -= tf_mean
xs = tf.matmul(xs, tf_components)
# map back
xs = tf.matmul(xs, tf.transpose(tf_components))
xs += tf_mean
xs = tf.reshape(xs, (-1, 28, 28, 1))
return model.model(xs)
if invert:
model.predict = new_predict
attack = CarliniL2(sess, model, batch_size=100, max_iterations=3000,
binary_search_steps=6, targeted=False,
initial_const=1)
N = 100
test_adv = attack.attack(data.test_data[:N], data.test_labels[:N])
print('accuracy',np.mean(np.argmax(sess.run(model.predict(tf.constant(data.test_data,dtype=np.float32))),axis=1)==np.argmax(data.test_labels,axis=1)))
print(list(test_adv[0].flatten()))
print('dist',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
it = np.argmax(sess.run(model.predict(tf.constant(test_adv))),axis=1)
print('success',np.mean(it==np.argmax(data.test_labels,axis=1)[:N]))
def run(Data, Model, path):
sess = K.get_session()
K.set_learning_phase(False)
data, model = Data(), Model(path)
if Data == MNIST:
attack = CarliniL2(sess,
model,
batch_size=100,
max_iterations=2000,
binary_search_steps=5,
initial_const=1.,
learning_rate=1e-1,
targeted=False)
else:
attack = CarliniL2(sess,
model,
batch_size=100,
max_iterations=200,
binary_search_steps=3,
initial_const=.01,
learning_rate=1e-2,
targeted=True,
confidence=2)
now = time.time()
for name, X, y in [["test", data.test_data, data.test_labels]]:
print("OKAY", name)
for k in range(0, len(y), 5000):
#if os.path.exists("tmp/"+path.split("/")[1]+"."+name+".adv.X."+str(k)+".npy"):
# print('skip',k)
# continue
now = time.time()
adv = attack.attack(X[k:k + 100], y[k:k + 100])
#print('time',time.time()-now)
#print('accuracy',np.mean(np.argmax(model.model.predict(adv),axis=1)==np.argmax(y[k:k+5000],axis=1)))
#print('mean distortion',np.mean(np.sum((adv-X[k:k+5000])**2,axis=(1,2,3))**.5))
np.save(
"/tmp/" + path.split("/")[1] + "." + name + ".adv.X." + str(k),
adv)
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 run_pca(Data, Model, path=None):
sess = K.get_session()
K.set_learning_phase(False)
data = Data()
model = Model(path)
shape = (-1, model.num_channels * model.image_size**2)
pca = sklearn.decomposition.PCA(n_components=shape[1])
pca.fit(data.train_data.reshape(shape))
print(pca.explained_variance_ratio_)
r_test = pca.transform(data.test_data.reshape(shape))
#attack = FGS(sess, model, eps=.3)
attack = CarliniL2(sess,
model,
batch_size=100,
max_iterations=1000,
binary_search_steps=2,
targeted=False,
initial_const=10)
N = 10000
#test_adv = attack.attack(data.test_data[:N], data.test_labels[:N])
test_adv = np.load("tmp/outlieradvtest.npy")
r_test_adv = pca.transform(test_adv[:N].reshape(shape))
fig = plt.figure(figsize=(4, 3))
fig.subplots_adjust(bottom=0.17, left=.19)
plt.xlabel('Component Number')
plt.ylabel('Mean Absolute Value (log scale)')
plt.semilogy(range(r_test.shape[1]),
np.mean(np.abs(r_test), axis=0),
label='Valid')
plt.semilogy(range(r_test_adv.shape[1]),
np.mean(np.abs(r_test_adv), axis=0),
label='Adversarial')
plt.legend()
pp = PdfPages('/tmp/a.pdf')
plt.savefig(pp, format='pdf')
pp.close()
plt.show()
def run_filter(Data, Model, path):
K.set_learning_phase(False)
data = Data()
model = Model(path)
model2 = Model(path)
def new_predict(xs):
print(xs.get_shape())
if 'mnist' in path:
xs = tf.nn.conv2d(xs, tf.constant(np.ones((3,3,1,1))/9,dtype=tf.float32),
[1,1,1,1], "SAME")
else:
xs = tf.nn.conv2d(xs, tf.constant(np.ones((3,3,3,3))/9,dtype=tf.float32),
[1,1,1,1], "SAME")
return model2.model(xs)
model2.predict = new_predict
sess = K.get_session()
#dist 1.45976
attack = CarliniL2(sess, model2, batch_size=100, max_iterations=3000,
binary_search_steps=4, targeted=False, confidence=0,
initial_const=10)
N = 100
test_adv = attack.attack(data.test_data[:N], data.test_labels[:N])
print('accuracy of original model',np.mean(np.argmax(sess.run(model.predict(tf.constant(data.test_data,dtype=np.float32))),axis=1)==np.argmax(data.test_labels,axis=1)))
print('accuracy of blurred model',np.mean(np.argmax(sess.run(model.predict(tf.constant(data.test_data,dtype=np.float32))),axis=1)==np.argmax(data.test_labels,axis=1)))
print('dist',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
#it = np.argmax(sess.run(model.predict(tf.constant(test_adv))),axis=1)
#print('success of unblured',np.mean(it==np.argmax(data.test_labels,axis=1)[:N]))
it = np.argmax(sess.run(model2.predict(tf.constant(test_adv))),axis=1)
print('success of blured',np.mean(it==np.argmax(data.test_labels,axis=1)[:N]))
def run_test(Data, Model, path):
sess = K.get_session()
K.set_learning_phase(False)
data = Data()
model = Model(path)
N = 1000
X = data.train_data[np.random.choice(np.arange(len(data.train_data)),
N,
replace=False)].reshape((N, -1))
#Y = data.train_data[np.random.choice(np.arange(len(data.train_data)), N, replace=False)].reshape((N,-1))
Y = data.test_data[np.random.choice(np.arange(len(data.test_data)),
N,
replace=False)].reshape((N, -1))
#attack = FGS(sess, model, N, .275)
attack = CarliniL2(sess,
model,
batch_size=100,
binary_search_steps=2,
initial_const=1,
targeted=False,
max_iterations=500)
idx = np.random.choice(np.arange(len(data.test_data)), N, replace=False)
Y = attack.attack(data.test_data[idx], data.test_labels[idx]).reshape(
(N, -1))
iterations = 1000
sigma2 = 100
mmd2u, mmd2u_null, p_value = kernel_two_sample_test(X,
Y,
iterations=iterations,
kernel_function='rbf',
gamma=1.0 / sigma2,
verbose=True)
def run_evaluation(Data, Model, path, num_epochs, name):
data = Data()
#train(Model, data, 10, path, num_epochs=num_epochs)
sess = K.get_session()
K.set_learning_phase(False)
model = Model(path)
#attack = FGS(sess, model)
attack = CarliniL2(sess,
model,
batch_size=100,
max_iterations=3000,
binary_search_steps=3,
targeted=True,
initial_const=10,
learning_rate=1e-2)
""" # uncomment to run the training phase
train_adv = attack.attack(data.train_data, data.train_labels)
np.save("tmp/"+name+"outlieradvtrain",train_adv)
train_adv = np.load("tmp/"+name+"outlieradvtrain.npy")
data.train_data = np.concatenate((data.train_data, train_adv))
data.train_labels = np.concatenate((data.train_labels, np.zeros(data.train_labels.shape, dtype=np.float32)))
data.train_labels = np.pad(data.train_labels, [[0, 0], [0, 1]], mode='constant')
data.train_labels[data.train_labels.shape[0]//2:,10] = 1
validation_adv = attack.attack(data.validation_data, data.validation_labels)
np.save("tmp/"+name+"outlieradvvalidation",validation_adv)
validation_adv = np.load("tmp/"+name+"outlieradvvalidation.npy")
data.validation_data = np.concatenate((data.validation_data, validation_adv))
data.validation_labels = np.concatenate((data.validation_labels, np.zeros(data.validation_labels.shape, dtype=np.float32)))
data.validation_labels = np.pad(data.validation_labels, [[0, 0], [0, 1]], mode='constant')
data.validation_labels[data.validation_labels.shape[0]//2:,10] = 1
test_adv = attack.attack(data.test_data, data.test_labels)
np.save("tmp/"+name+"outlieradvtest",test_adv)
test_adv = np.load("tmp/"+name+"outlieradvtest.npy")
data.test_data = np.concatenate((data.test_data, test_adv))
data.test_labels = np.concatenate((data.test_labels, np.zeros(data.test_labels.shape, dtype=np.float32)))
data.test_labels = np.pad(data.test_labels, [[0, 0], [0, 1]], mode='constant')
data.test_labels[data.test_labels.shape[0]//2:,10] = 1
train(Model, data, 11, path+"_advtraining", num_epochs=num_epochs)
data1 = Data() # just need a reference, this is a bit ugly to do
data2 = Data() # just need a reference, this is a bit ugly to do
idxs = list(range(len(data.train_data)))
random.shuffle(idxs)
data1.train_data = data.train_data[idxs[:len(idxs)//2]]
data2.train_data = data.train_data[idxs[len(idxs)//2:]]
data1.train_labels = data.train_labels[idxs[:len(idxs)//2],:]
data2.train_labels = data.train_labels[idxs[len(idxs)//2:],:]
idxs = list(range(len(data.validation_data)))
random.shuffle(idxs)
data1.validation_data = data.validation_data[idxs[:len(idxs)//2]]
data2.validation_data = data.validation_data[idxs[len(idxs)//2:]]
data1.validation_labels = data.validation_labels[idxs[:len(idxs)//2]]
data2.validation_labels = data.validation_labels[idxs[len(idxs)//2:]]
idxs = list(range(len(data.test_data)))
random.shuffle(idxs)
data1.test_data = data.test_data[idxs[:len(idxs)//2]]
data2.test_data = data.test_data[idxs[len(idxs)//2:]]
data1.test_labels = data.test_labels[idxs[:len(idxs)//2]]
data2.test_labels = data.test_labels[idxs[len(idxs)//2:]]
train(Model, data1, 11, path+"_advtraining-left", num_epochs=num_epochs)
train(Model, data2, 11, path+"_advtraining-right", num_epochs=num_epochs)
#"""
K.set_learning_phase(False)
rmodel = Model(num_labels=11).model
rmodel.load_weights(path + "_advtraining")
if name == "cifar":
rmodel = Wrap(rmodel, 32, 3, 11)
else:
rmodel = Wrap(rmodel, 28, 1, 11)
rmodel1 = Model(num_labels=11).model
rmodel1.load_weights(path + "_advtraining-left")
if name == "cifar":
rmodel1 = Wrap(rmodel1, 32, 3, 11)
else:
rmodel1 = Wrap(rmodel1, 28, 1, 11)
rmodel2 = Model(num_labels=11).model
rmodel2.load_weights(path + "_advtraining-right")
if name == "cifar":
rmodel2 = Wrap(rmodel2, 32, 3, 11)
else:
rmodel2 = Wrap(rmodel2, 28, 1, 11)
rmodel2.model.summary()
attack2 = CarliniL2(sess,
rmodel,
batch_size=100,
max_iterations=2000,
confidence=.1,
binary_search_steps=3,
targeted=True,
initial_const=10,
learning_rate=1e-2)
#test_adv = np.load("tmp/outlieradvtest.npy")
#print('qq',np.mean(rmodel.model.predict_classes(test_adv)==10))
N = 100
targets = utils.get_labs(data.test_labels[:100])
#"""
test_adv = attack.attack(data.test_data[:N], targets)
print(
'mean distortion',
np.mean(
np.sum((test_adv - data.test_data[:N])**2, axis=(1, 2, 3))**.5))
print('model predict', np.argmax(model.model.predict(test_adv), axis=1))
print('rmodel predict', np.argmax(rmodel.model.predict(test_adv), axis=1))
#"""
targets2 = np.zeros((N, 11))
targets2[:, :10] = targets
test_adv = attack2.attack(data.test_data[:N], targets2)
print(list(test_adv[0].flatten()))
print(
'mean distortion',
np.mean(
np.sum((test_adv - data.test_data[:N])**2, axis=(1, 2, 3))**.5))
a = (np.argmax(model.model.predict(test_adv), axis=1))
#print(a)
print('summary', np.mean(a == np.argmax(targets, axis=1)),
np.mean(a == 10))
a = (np.argmax(rmodel.model.predict(test_adv), axis=1))
#print(a)
print('summary', np.mean(a == np.argmax(targets, axis=1)),
np.mean(a == 10))
a = (np.argmax(rmodel1.model.predict(test_adv), axis=1))
#print(a)
print('summary', np.mean(a == np.argmax(targets, axis=1)),
np.mean(a == 10))
a = (np.argmax(rmodel2.model.predict(test_adv), axis=1))
#print(a)
print('summary', np.mean(a == np.argmax(targets, axis=1)),
np.mean(a == 10))
model.add(Conv2D(32, (2, 2), activation="relu", padding="same"))
model.add(Conv2D(128, (2, 2), activation="relu", padding="same"))
model.add(Conv2D(128, (1, 1), activation="relu", padding="same"))
model.add(Flatten())
model.add(Dense(10, activation="softmax"))
model.summary()
#model = model_mnist(input_image=Input(shape=(28, 28, 1)))
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["acc"])
model.load_weights('./cifar100/mnist_cnn_G08.hdf5')
with tf.Session() as sess:
data, model1 = MNIST_data(), MNISTModel()
attack = CarliniL2(sess, model1, batch_size=100, max_iterations=1000,
confidence=0, boxmin=0, boxmax=1)
inputs, targets = generate_data(data, samples=N, targeted=True,
start=0, inception=False)
print(targets)
adv = attack.attack(inputs, targets)
# MNISTデータの表示
W = 10 # 横に並べる個数
H = 10 # 縦に並べる個数
fig = plt.figure(figsize=(H, W))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1.0, hspace=0.05, wspace=0.05)
for i in range(W*H):
ax1 = fig.add_subplot(H, W, i + 1, xticks=[], yticks=[])
ax1.imshow(x_test[i].reshape((28, 28)), cmap='gray')
plt.savefig('./cifar100/mnist_x_test100_G08.jpg')
def run_kde(Data, Model, path):
global DECONST
sess = K.get_session()
K.set_learning_phase(False)
data, model = Data(), Model(path)
model2 = Model(path)
# TODO: hidden_layer -> selected layer
layer_name = "activation_7"
hidden_layer = pop_layer(model2.model, layer_name)
#hidden_layer = pop(model2.model) # once to remove dense(10)
#hidden_layer = pop(hidden_layer) # once to remove ReLU
#compute_optimal_sigma(sess, model, hidden_layer, data)
#MNIST SIGMA: 20
removed_cols = []
for i in range(10):
removed_cols.extend(
get_removed_cols(
hidden_layer,
data.train_data[np.argmax(data.train_labels, axis=1) == i]))
removed_cols = list(set(removed_cols))
de = [
DensityEstimate(
sess,
hidden_layer,
data.train_data[np.argmax(data.train_labels, axis=1) == i],
model.image_size,
model.num_channels,
removed_cols,
sigma=0.864) for i in range(10)
]
#de2 = [DensityEstimate(sess, hidden_layer, data.train_data[np.argmax(data.train_labels,axis=1)==i], model.image_size, model.num_channels, sigma=0.864) for i in range(10)]
de2 = de
p = tf.placeholder(
tf.float32,
(None, model.image_size, model.image_size, model.num_channels))
#print(np.log(de[0].predict(data.test_data[:10])))
#print(sess.run(rmodel.predict(p)[1], {p: data.test_data[:10]}))
#exit(0)
N = 9
#print(model.model.predict(data.train_data[:N]))
#print(hidden_layer.predict(data.train_data[:N]))
adv_candid = []
jumped = False
adv_labels = np.zeros((9, 10))
for i in range(0, 10):
if i == TARGET_CLASS:
jumped = True
continue
adv_candid.extend(
data.test_data[np.argmax(data.test_labels, axis=1) == i][:1])
if jumped:
adv_labels[i - 1][TARGET_CLASS] = 1
else:
adv_labels[i][TARGET_CLASS] = 1
adv_candid = np.array(adv_candid)
#for i in range(10):
# for j in range(N):
# print(de[i].predict(data.train_data[j:j+1])) # N
#start_density = estimate_density_full(model, de, data.test_data[M:M+N])+1e-30
start_density = estimate_density_full(model, de, adv_candid) + 1e-30
print("starting density", -np.log(start_density))
#print("starting density", -start_density)
DECONST = -np.log(start_density)
#DECONST = -start_density
DECONST = np.median(DECONST)
#DECONST = 0
print("DECONST", DECONST)
#DECONST = -1
l = np.zeros((N, 10))
#l[np.arange(N),np.random.random_integers(0,9,N)] = 1
for i in range(N):
r = np.random.random_integers(0, 9)
while r == np.argmax(data.test_labels[i]):
r = np.random.random_integers(0, 9)
l[i, r] = 1
l = adv_labels
print(l)
attack1 = CarliniL2(sess,
model,
batch_size=1,
max_iterations=3000,
binary_search_steps=3,
initial_const=1.0,
learning_rate=1e-1,
targeted=True)
attack2 = CarliniL2New(sess,
model,
batch_size=1,
max_iterations=60000,
binary_search_steps=5,
initial_const=1.0,
learning_rate=1e-2,
targeted=True,
extra_loss=extra_loss(de2, TARGET_CLASS),
debug_extra_loss=debug_extra_loss(
de2, TARGET_CLASS),
de=de2)
#l = data.test_labels[:N]
#l = np.zeros((N,10))
#l[np.arange(N),1] = 1
print("RUN PHASE 1")
#adv = attack1.attack(data.test_data[M:M+N], l)
adv = attack1.attack(adv_candid, l)
#print('mean distortion',np.mean(np.sum((adv-data.test_data[M:M+N])**2,axis=(1,2,3))**.5))
print('mean distortion',
np.mean(np.sum((adv - adv_candid)**2, axis=(1, 2, 3))**.5))
print("RUN PHASE 2")
#adv = attack2.attack(data.test_data[M:M+N], adv, l)
adv = attack2.attack(adv_candid, adv, l)
#np.save("/tmp/q"+str(M),adv)
np.save("./adv/adv_mnist_cnw_target_{}".format(TARGET_CLASS), adv)
#adv = np.load("/tmp/qq.npy")
#print('labels',np.mean(np.argmax(sess.run(model.predict(p), {p: adv}),axis=1)==l))
print('labels')
print(np.argmax(l, axis=1))
print(np.argmax(sess.run(model.predict(p), {p: adv}), axis=1))
print(np.argmax(model.model.predict(adv), axis=1))
#print('mean distortion',np.mean(np.sum((adv-data.test_data[M:M+N])**2,axis=(1,2,3))**.5))
print('mean distortion',
np.mean(np.sum((adv - adv_candid)**2, axis=(1, 2, 3))**.5))
#a = estimate_density_full(model, de, data.test_data[M:M+N])+1e-30
a = estimate_density_full(model, de, adv_candid) + 1e-30
b = estimate_density_full(model, de, adv) + 1e-30
#print(data.test_data.shape)
#print(adv.shape)
show(adv)
print('de of test', np.mean(-np.log(a)))
print('de of adv', np.mean(-np.log(b)))
print('better ratio', np.mean(np.array(a) > np.array(b)))
exit(0)
#density = gaussian_kde(np.array(np.log(a))-np.array(np.log(b)))
#density_a = gaussian_kde(np.log(a))
#density_b = gaussian_kde(np.log(b))
xs = np.linspace(-25, 25, 200)
fig = plt.figure(figsize=(4, 3))
fig.subplots_adjust(bottom=0.17, left=.15, right=.85)
plt.xlabel('log(KDE(valid))-log(KDE(adversarial))')
plt.ylabel('Occurrances')
#plt.hist(np.log(a),100)
#plt.hist(np.log(b),100)
plt.hist(np.log(a) - np.log(b), 100)
#plt.hist(np.array(np.log(a))-np.array(np.log(b)),100)
#a = plt.plot(xs,density_a(xs), 'r--',color='blue', label='Valid')
#b = plt.plot(xs,density_b(xs), color='red', label='Adversarial')
#plt.plot(xs,density(xs))
#plt.legend(handles=[a[0], b[0]])
pp = PdfPages('/tmp/a.pdf')
plt.savefig(pp, format='pdf')
pp.close()
def run_nn_detection(Data, path):
data = Data()
sess = K.get_session()
K.set_learning_phase(False)
model_with_detector = ResnetBuilder.build_resnet_32((3, 32, 32), 10,
with_detector=2, activation=False)
model_with_detector.save_weights("/tmp/q")
model_with_detector.load_weights("models/cifar-layerdetect-37-0")
N = 10#len(data.test_data)//100
""" # uncomment to generate adversarial testing data
model = ResnetBuilder.build_resnet_32((3, 32, 32), 10, activation=False)
model.load_weights("models/cifar-resnet")
model = Wrap(model)
#attack = FGS(sess, model)
attack = CarliniL2(sess, model, batch_size=100, binary_search_steps=3,
initial_const=0.1, max_iterations=3000, learning_rate=0.005,
confidence=0, targeted=False)
for i in range(0,N,1000):
test_adv = attack.attack(data.test_data[i:i+100], data.test_labels[i:i+100])
np.save("tmp/testadv"+path.split("/")[1]+str(i), test_adv)
#"""
test_adv = []
for i in range(0,N,1000):
test_adv.extend(np.load("tmp/testadv"+path.split("/")[1]+str(i)+".npy"))
test_adv = np.array(test_adv)
print('Accuracy of model on test set',np.mean(np.argmax(model_with_detector.predict(data.test_data)[0],axis=1)==np.argmax(data.test_labels,axis=1)))
print('Accuracy of model on adversarial data',np.mean(np.argmax(model_with_detector.predict(test_adv)[0],axis=1)==np.argmax(data.test_labels,axis=1)))
print('Probaility detects valid data as valid',np.mean(model_with_detector.predict(data.test_data)[1]<=0))
print('Probability detects adversarail data as adversarial',np.mean(model_with_detector.predict(test_adv)[1]>0))
xs = tf.placeholder(tf.float32, [None, 32, 32, 3])
rmodel = RobustModel(model_with_detector)
preds = rmodel.predict(xs)
y1 = np.argmax(sess.run(preds, {xs: data.test_data[:N]}),axis=1)
print('Robust model accuracy on test dat',np.mean(y1==np.argmax(data.test_labels[:N],axis=1)))
print('Probability robust model detects valid data as adversarial', np.mean(y1==10))
y2 = np.argmax(sess.run(preds, {xs: test_adv}),axis=1)
print('Probability robust model detects adversarial data as adversarial', np.mean(y2==10))
attack = CarliniL2(sess, rmodel, batch_size=10, binary_search_steps=3,
initial_const=0.1, max_iterations=300, learning_rate=0.01,
confidence=0, targeted=True)
targets = np.argmax(model_with_detector.predict(test_adv[:N])[0],axis=1)
realtargets = np.zeros((N, 11))
realtargets[np.arange(N),targets] = 1
np.save("tmp/adaptiveattack",attack.attack(data.test_data[:N], realtargets))
adv = np.load("tmp/adaptiveattack.npy")
print('Accuracy on adversarial data',np.mean(np.argmax(model_with_detector.predict(adv)[0],axis=1)==np.argmax(data.test_labels,axis=1)))
print('Probability detector detects adversarial data as adversarial',np.mean(model_with_detector.predict(adv)[1]>0))
d=np.sum((adv-data.test_data[:N])**2,axis=(1,2,3))**.5
print("mean distortion attacking robust model", np.mean(d))
d=np.sum((test_adv[:N]-data.test_data[:N])**2,axis=(1,2,3))**.5
print("mean distortion attacking unsecurred model", np.mean(d))
model_with_detector_2 = ResnetBuilder.build_resnet_32((3, 32, 32), 10,
with_detector=2, activation=False)
model_with_detector_2.load_weights("models/cifar-layerdetect-42-0")
print('Accuracy on adversarial data',np.mean(np.argmax(model_with_detector_2.predict(adv)[0],axis=1)==np.argmax(data.test_labels,axis=1)))
print('Probability detector detects adversarial data as adversarial',np.mean(model_with_detector_2.predict(adv)[1]>0))
generated_img) # preprocess image
enc_gen, enc_gen_layers = stn.encoder.encode(generated_img)
if data_set == "cifar10":
classifier = Model("eval", raw_cifar.train_images)
classifier._build_model(adv_img, label, reuse=False, conf=0.1)
adv_loss = - classifier.target_loss
adv_acc = classifier.accuracy
adv_acc_y = tf.cast(classifier.correct_prediction, tf.float32)
classifier._build_model(content, label, reuse=True)
normal_loss = - classifier.target_loss
norm_acc = classifier.accuracy
logits = classifier.pre_softmax
pgd_attack = LinfPGDAttack(classifier.xent, content, label, epsilon=0.25 *
255, num_steps=200, step_size=0.05*255, random_start=True)
CarliniL2.pgd_attack()
elif data_set == "imagenet":
classifier = build_imagenet_model(
adv_img_bgr, label, conf=0.1, shrink_class=shrink_class)
adv_loss = - classifier.target_loss
adv_acc = classifier.accuracy
adv_acc_y = classifier.acc_y
adv_acc_y_5 = classifier.acc_y_5
#logits = classifier.logits
content_bgr = tf.reverse(
content, axis=[-1]) # switch RGB to BGR
classifier = build_imagenet_model(
content_bgr, label, reuse=True, shrink_class=shrink_class)
def test(Model, data, path):
keras.backend.set_learning_phase(False)
model = make_model(Model, dropout=False)
model.load_weights(path)
modeld = make_model(Model, dropout=True)
modeld.load_weights(path)
guess = model.predict(data.test_data)
print(guess[:10])
print(
'Accuracy wihtout dropout',
np.mean(
np.argmax(guess, axis=1) == np.argmax(data.test_labels, axis=1)))
guess = modeld.predict(data.test_data)
print(
'Accuracy with dropout',
np.mean(
np.argmax(guess, axis=1) == np.argmax(data.test_labels, axis=1)))
sess = keras.backend.get_session()
N = 10
labs = get_labs(data.test_data[:N])
print(labs)
print('good?', np.sum(labs * data.test_labels[:N]))
attack = CarliniL2(sess,
Wrap(model),
batch_size=N,
max_iterations=1000,
binary_search_steps=3,
learning_rate=1e-1,
initial_const=1,
targeted=True,
confidence=0)
adv = attack.attack(data.test_data[:N], labs)
guess = model.predict(adv)
print('average distortion',
np.mean(np.sum((data.test_data[:N] - adv)**2, axis=(1, 2, 3))**.5))
print(guess[:10])
print("Test data")
valid_u = compute_u(sess, modeld, data.test_data[:N])
print("Adversarial examples")
valid_u = compute_u(sess, modeld, adv)
# The below attack may not even be necessary for CIFAR
# the adversarial examples generated with (3,1000,1e-1) have a lower mean
# uncertenty than the test images, but again with a 3x increase in distortion.
if ISMNIST:
p = tf.placeholder(tf.float32, (None, 28, 28, 1))
else:
p = tf.placeholder(tf.float32, (None, 32, 32, 3))
r = differentable_u(modeld, p, 100)
models = []
for _ in range(20):
m = make_model(Model, dropout=True, fixed=True)
m.load_weights(path)
models.append(m)
#r2 = differentable_u_multiple(models, p)
#print('uncertenty on test data', np.mean((sess.run(r, {p: data.test_data[:N]}))))
#print('uncertenty on test data (multiple models)', np.mean((sess.run(r2, {p: data.test_data[:N]}))))
#print('labels on robust model', np.argmax(sess.run(robustmodel.predict(p), {p: data.test_data[:100]}),axis=1))
attack = CarliniL2Multiple(sess, [Wrap(m) for m in models],
batch_size=10,
binary_search_steps=4,
initial_const=1,
max_iterations=1000,
confidence=1,
targeted=True,
abort_early=False,
learning_rate=1e-1)
#z = np.zeros((N, 10))
#z[np.arange(N),np.random.random_integers(0,9,N)] = 1
#z[np.arange(N),(9, 3, 0, 8, 7, 3, 4, 1, 6, 4)] = 1
print(z)
#qq = (3, 2, 1, 18, 4, 8, 11, 0, 61, 7)
#np.save("images/mnist_dropout", attack.attack(data.test_data[qq,:,:,:],
# np.pad(np.roll(data.test_labels[qq,:],1,axis=1), [(0, 0), (0, 0)], 'constant')))
#exit(0)
adv = attack.attack(data.test_data[:N], labs)
#adv = attack.attack(data.test_data[:N], data.test_labels[:N])
np.save("/tmp/dropout_adv_" + str(ISMNIST), adv)
#adv = np.load("/tmp/qq.npy")
guess = model.predict(adv)
print('normal predictions', guess)
print('average distortion',
np.mean(np.sum((data.test_data[:N] - adv)**2, axis=(1, 2, 3))**.5))
print('normal label predictions', np.argmax(guess, axis=1))
for m in models:
print('model preds', np.argmax(m.predict(adv), axis=1))
print(
'Model accuracy on adversarial examples',
np.mean(
np.argmax(guess, axis=1) == np.argmax(data.test_labels[:N],
axis=1)))
adv_u = compute_u(sess, modeld, adv)
#print('differentable uncertienty',np.mean((sess.run(r, {p: adv}))))
print('Targetted adversarial examples success rate',
np.mean(np.argmax(guess, axis=1) == np.argmax(z, axis=1)))
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
"""
fig = plt.figure(figsize=(4,3))
fig.subplots_adjust(bottom=0.15,left=.15)
a=plt.hist(adv_u, 100, log=True, label="Adversarial (FGS)")
b=plt.hist(valid_u, 100, log=True, label="Valid")
plt.xlabel('Uncertainty')
plt.ylabel('Occurrances (log scaled)')
plt.legend()
"""
fig = plt.figure(figsize=(4, 3))
fig.subplots_adjust(bottom=0.15, left=.15)
b = plt.hist(valid_u - adv_u, 100, label="Valid")
plt.xlabel('U(valid)-U(adversarial)')
plt.ylabel('Occurrances')
pp = PdfPages('/tmp/a.pdf')
plt.savefig(pp, format='pdf')
pp.close()
plt.show()
def run_kde(Data, Model, path):
global DECONST
sess = K.get_session()
K.set_learning_phase(False)
data, model = Data(), Model(path)
model2 = Model(path)
hidden_layer = pop(model2.model) # once to remove dense(10)
hidden_layer = pop(hidden_layer) # once to remove ReLU
#compute_optimal_sigma(sess, model, hidden_layer, data)
#MNIST SIGMA: 20
de = [
DensityEstimate(
sess,
hidden_layer,
data.train_data[np.argmax(data.train_labels, axis=1) == i],
model.image_size,
model.num_channels,
sigma=20) for i in range(10)
]
de2 = [
DensityEstimate(
sess,
hidden_layer,
data.train_data[np.argmax(data.train_labels, axis=1) == i][:100],
model.image_size,
model.num_channels,
sigma=20) for i in range(10)
]
p = tf.placeholder(
tf.float32,
(None, model.image_size, model.image_size, model.num_channels))
#print(np.log(de[0].predict(data.test_data[:10])))
#print(sess.run(rmodel.predict(p)[1], {p: data.test_data[:10]}))
#exit(0)
N = 1
print(model.model.predict(data.train_data[:N]))
print(hidden_layer.predict(data.train_data[:N]))
for i in range(10):
print(de[i].predict(data.train_data[:N]))
start_density = estimate_density_full(model, de,
data.test_data[M:M + N]) + 1e-30
print("starting density", np.log(start_density))
DECONST = -np.log(start_density)
l = np.zeros((N, 10))
#l[np.arange(N),np.random.random_integers(0,9,N)] = 1
for i in range(N):
r = np.random.random_integers(0, 9)
while r == np.argmax(data.test_labels[i]):
r = np.random.random_integers(0, 9)
l[i, r] = 1
attack1 = CarliniL2(sess,
model,
batch_size=1,
max_iterations=3000,
binary_search_steps=3,
initial_const=1.0,
learning_rate=1e-1,
targeted=True)
attack2 = CarliniL2New(sess,
model,
batch_size=1,
max_iterations=10000,
binary_search_steps=5,
initial_const=1.0,
learning_rate=1e-2,
targeted=True,
extra_loss=extra_loss(de2, np.argmax(l)))
#l = data.test_labels[:N]
#l = np.zeros((N,10))
#l[np.arange(N),1] = 1
print("RUN PHASE 1")
adv = attack1.attack(data.test_data[M:M + N], l)
print(
'mean distortion',
np.mean(
np.sum((adv - data.test_data[M:M + N])**2, axis=(1, 2, 3))**.5))
print("RUN PHASE 2")
adv = attack2.attack(data.test_data[M:M + N], adv, l)
np.save("/tmp/q" + str(M), adv)
#adv = np.load("/tmp/qq.npy")
print(
'labels',
np.mean(np.argmax(sess.run(model.predict(p), {p: adv}), axis=1) == l))
print(
'mean distortion',
np.mean(
np.sum((adv - data.test_data[M:M + N])**2, axis=(1, 2, 3))**.5))
a = estimate_density_full(model, de, data.test_data[M:M + N]) + 1e-30
b = estimate_density_full(model, de, adv) + 1e-30
show(adv)
print('de of test', np.mean(np.log(a)))
print('de of adv', np.mean(np.log(b)))
print('better ratio', np.mean(np.array(a) > np.array(b)))
exit(0)
#density = gaussian_kde(np.array(np.log(a))-np.array(np.log(b)))
#density_a = gaussian_kde(np.log(a))
#density_b = gaussian_kde(np.log(b))
xs = np.linspace(-25, 25, 200)
fig = plt.figure(figsize=(4, 3))
fig.subplots_adjust(bottom=0.17, left=.15, right=.85)
plt.xlabel('log(KDE(valid))-log(KDE(adversarial))')
plt.ylabel('Occurrances')
#plt.hist(np.log(a),100)
#plt.hist(np.log(b),100)
plt.hist(np.log(a) - np.log(b), 100)
#plt.hist(np.array(np.log(a))-np.array(np.log(b)),100)
#a = plt.plot(xs,density_a(xs), 'r--',color='blue', label='Valid')
#b = plt.plot(xs,density_b(xs), color='red', label='Adversarial')
#plt.plot(xs,density(xs))
#plt.legend(handles=[a[0], b[0]])
pp = PdfPages('/tmp/a.pdf')
plt.savefig(pp, format='pdf')
pp.close()
plt.show()
