def main(_):
testX_adv = utils.readdata_np(ADV_DATA_PATH)
testy_adv = utils.readdata_np(ADV_LABEL_PATH)
test_adv_input = utils.DataProducer(testX_adv, testy_adv, 128, 30,
"test_adv")
for j in range(10):
for i, x, y in test_adv_input.next_batch():
print i
test_adv_input.reset_cursor()
def test(name = 'train'):
if name == 'train':
#read dataset
trainX = utils.readdata_np(TRAIN_DATA_PATH)
trainy = utils.readdata_np(TRAIN_LABEL_PATH)
testX = utils.readdata_np(TEST_DATA_PATH)
testy = utils.readdata_np(TEST_LABEL_PATH)
train_input = utils.DataProducer(trainX, trainy, FLAGS.batch_size, FLAGS.epoches, "train")
test_input = utils.DataProducer(testX, testy, FLAGS.batch_size, FLAGS.epoches, "test")
input_dim = trainX.shape[1]
_config = {
'n_epoches':FLAGS.epoches,
'batch_size':FLAGS.batch_size,
'output_dim': FLAGS.output_dims,
'input_dim' : input_dim,
'hidden_dims' : list(map(int, FLAGS.hidden_dims.split(','))),
'adv_k' : FLAGS.adv_k,
'K' : FLAGS.K, #recommned K =sqrt(input_dim)
'L' : FLAGS.L
}
config = utils.ParamWrapper(_config)
tf.reset_default_graph()
model_mlp = FFN_lh_model(config)
tf.set_random_seed(1234)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
if name == 'train':
train(sess, model_mlp, train_input, test_input, config)
if name == 'evaluate':
mal_data = utils.readdata_np(MAL_DATA_PATH)
ben_data = utils.readdata_np(BEN_DATA_PATH)
input_dim = mal_data.shape[1]
_config = {
'n_epoches':FLAGS.epoches,
'batch_size':FLAGS.batch_size,
'output_dim': FLAGS.output_dims,
'input_dim' : input_dim,
'hidden_dims' : list(map(int, FLAGS.hidden_dims.split(','))),
'adv_k' : FLAGS.adv_k,
'K' : FLAGS.K, #recommned K =sqrt(input_dim)
'L' : FLAGS.L
}
config = utils.ParamWrapper(_config)
tf.reset_default_graph()
model_mlp = FFN_lh_model(config)
tf.set_random_seed(1234)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
evaluate(sess, model_mlp, mal_data, ben_data)
if name == 'experiments':
ADV_SMPS_dir = './dataset/adv_smps/'
ADV_SMPS10= os.path.join(ADV_SMPS_dir, 'adv_smp10.data')
ADV_SMPS20 = os.path.join(ADV_SMPS_dir, 'adv_smp20.data')
ADV_SMPS30 = os.path.join(ADV_SMPS_dir, 'adv_smp30.data')
ADV_SMPS50 = os.path.join(ADV_SMPS_dir, 'adv_smp50.data')
ADV_SMPS80 = os.path.join(ADV_SMPS_dir, 'adv_smp80.data')
ADV_SMPS_OPT10 = os.path.join(ADV_SMPS_dir, 'adv_smps_opt10.data')
ADV_SMPS_OPT20 = os.path.join(ADV_SMPS_dir, 'adv_smps_opt20.data')
ADV_SMPS_OPT30 = os.path.join(ADV_SMPS_dir, 'adv_smps_opt30.data')
CLEAN_SMPS_label_path = os.path.join(ADV_SMPS_dir, 'clean_smp.label')
CLEAN_SMPS_path = os.path.join(ADV_SMPS_dir, 'clean_smp.data')
adv_smps10 = utils.readdata_np(ADV_SMPS10)
adv_smps20 = utils.readdata_np(ADV_SMPS20)
adv_smps30 = utils.readdata_np(ADV_SMPS30)
adv_smps50 = utils.readdata_np(ADV_SMPS50)
adv_smps80 = utils.readdata_np(ADV_SMPS80)
adv_smps_opt10 = utils.readdata_np(ADV_SMPS_OPT10)
adv_smps_opt20 = utils.readdata_np(ADV_SMPS_OPT20)
adv_smps_opt30 = utils.readdata_np(ADV_SMPS_OPT30)
adv_smps0 = utils.readdata_np(CLEAN_SMPS_path)
label = utils.readdata_np(CLEAN_SMPS_label_path)
test_bias = np.array([[1., 1]], dtype = np.float32)
input_dim = adv_smps0.shape[1]
_config = {
'n_epoches':FLAGS.epoches,
'batch_size':FLAGS.batch_size,
'output_dim': FLAGS.output_dims,
'input_dim' : input_dim,
'hidden_dims' : list(map(int, FLAGS.hidden_dims.split(','))),
'adv_k' : FLAGS.adv_k,
'K' : FLAGS.K, #recommned K =sqrt(input_dim)
'L' : FLAGS.L
}
config = utils.ParamWrapper(_config)
tf.reset_default_graph()
model_mlp = FFN_lh_model(config)
tf.set_random_seed(1234)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
model_path = os.path.join(FLAGS.save_dir, "model.ckpt")
check_p = tf.train.checkpoint_exists(model_path)
if check_p:
saver.restore(sess, model_path)
else:
print("No saved parameters")
with sess.as_default():
def _evl(X, y):
batches = X.shape[0] // FLAGS.batch_size + 1
accs = []
for mini_i in range(batches):
start_i = mini_i * FLAGS.batch_size
end_i = FLAGS.batch_size + start_i
if end_i > X.shape[0]:
end_i = X.shape[0]
_feed_dict ={
model_mlp.x : X[start_i : end_i],
model_mlp.y : y[start_i : end_i],
model_mlp.bias: test_bias,
model_mlp.is_training: False,
model_mlp.is_adv_training : False
}
acc = sess.run(model_mlp.acc, feed_dict = _feed_dict)
accs.append(acc)
return np.mean(accs)
acc1 = _evl(adv_smps0, label)
acc10 = _evl(adv_smps10, label)
acc20 = _evl(adv_smps20, label)
acc30 = _evl(adv_smps30, label)
acc50 = _evl(adv_smps50, label)
acc80 = _evl(adv_smps80, label)
acc10_opt = _evl(adv_smps_opt10, label)
acc20_opt = _evl(adv_smps_opt20, label)
acc30_opt = _evl(adv_smps_opt30, label)
MSG = "The predict accuracy of clean data and FGS adversarial examples with perturbations [0, 10, 20, 30, 50, 80] is {0:.5}, {1:.5},{2:.5},{3:.5}, {4:.5},{5:.5} respectively."
print(MSG.format(acc1, acc10, acc20, acc30, acc50, acc80))
MSG = "The C.W. adversarial examples with perturbations [10, 20, 30] is {0:.5},{1:.5} and {2:.5}"
print(MSG.format(acc10_opt, acc20_opt, acc30_opt))
def traindt(config, train_rpst, trainy):
robust_dt = nested_dt(config)
robust_dt.learn(train_rpst, trainy)
robust_dt.dump(save_dir)
def testdt(config, test_rpst, testy):
robust_dt = nested_lr(config)
robust_dt.load(save_dir)
acc = robust_dt.predict(test_rpst, testy)
return acc
if __name__ == '__main__':
trainX = utils.readdata_np(TRAIN_DATA_PATH)
trainy = utils.readdata_np(TRAIN_LABEL_PATH)[:, 0]
testX = utils.readdata_np(TEST_DATA_PATH)
testy = utils.readdata_np(TEST_LABEL_PATH)[:, 0]
testX_adv = utils.readdata_np(ADV_DATA_PATH)
testy_adv = utils.readdata_np(ADV_LABEL_PATH)[:, 0]
mal_data = utils.readdata_np(MAL_DATA_PATH)
ben_data = utils.readdata_np(BEN_DATA_PATH)
input_dim = trainX.shape[1]
_config = {
'save_dir': save_dir,
'input_dim': input_dim,
'K': K,
'L': L,
'max_depth': 2,
'is_rf': False