134 lines
4.8 KiB
Python
134 lines
4.8 KiB
Python
import tensorflow as tf
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import keras
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from keras.optimizers import SGD
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from Neural_Net_Module import dnn_model
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import csv
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from numpy import shape
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import numpy as np
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import matplotlib.pyplot as plt
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batch_size=32
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nb_epoch=15
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eps=0.5
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gamma=80
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def scaled_gradient(x, y, predictions):
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#loss: the mean of loss(cross entropy)
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loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predictions, labels=y))
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grad, = tf.gradients(loss, x)
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signed_grad = tf.sign(grad)
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return grad, signed_grad
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if __name__ == '__main__':
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if keras.backend.image_dim_ordering() != 'tf':
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keras.backend.set_image_dim_ordering('tf')
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sess = tf.Session()
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keras.backend.set_session(sess)
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with open('normal.csv', 'r') as csvfile:
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reader = csv.reader(csvfile)
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rows = [row for row in reader]
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rows=np.array(rows, dtype=float)
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data=rows
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label=np.zeros((200,1))
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with open('sag.csv', 'r') as csvfile:
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reader = csv.reader(csvfile)
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rows = [row for row in reader]
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rows=np.array(rows, dtype=float)
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data=np.concatenate((data, rows))
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labels=np.ones((200,1))
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label=np.concatenate((label, labels))
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with open('distortion.csv', 'r') as csvfile:
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reader = csv.reader(csvfile)
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rows = [row for row in reader]
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rows=np.array(rows, dtype=float)
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data=np.concatenate((data, rows))
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labels=2*np.ones((200,1))
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label=np.concatenate((label, labels))
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with open('impulse.csv', 'r') as csvfile:
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reader = csv.reader(csvfile)
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rows = [row for row in reader]
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rows=np.array(rows, dtype=float)
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data=np.concatenate((data, rows))
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labels=3*np.ones((200,1))
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label=np.concatenate((label, labels))
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label=label.reshape(-1,1)
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label=keras.utils.to_categorical(label, num_classes=None)
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print("Input label shape", shape(label))
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print("Input data shape", shape(data))
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index = np.arange(len(label))
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np.random.shuffle(index)
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label = label[index]
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data = data[index]
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trX=data[:600]
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trY=label[:600]
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teX=data[600:]
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teY=label[600:]
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x = tf.placeholder(tf.float32, shape=(None, 1000))
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y = tf.placeholder(tf.float32, shape=(None, 4))
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model = dnn_model(input_dim=1000)
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predictions = model(x)
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sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
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model.compile(loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.Adadelta(),
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metrics=['accuracy'])
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model.fit(trX, trY, batch_size=batch_size, epochs=nb_epoch, shuffle=True) # validation_split=0.1
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# model.save_weights('dnn_clean.h5')
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score = model.evaluate(teX, teY, verbose=0)
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print('Test loss:', score[0])
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print('Test accuracy:', score[1])
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with sess.as_default():
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adv_sample=[]
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counter = 0
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# Initialize the SGD optimizer
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grad, sign_grad = scaled_gradient(x, y, predictions)
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for q in range(200):
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if counter % 50 == 0 and counter > 0:
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print("Attack on samples" + str(counter))
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X_new_group=np.copy(teX[counter])
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gradient_value, signed_grad = sess.run([grad, sign_grad], feed_dict={x: X_new_group.reshape(-1,1000),
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y: teY[counter].reshape(-1,4),
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keras.backend.learning_phase(): 0})
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saliency_mat = np.abs(gradient_value)
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saliency_mat = (saliency_mat > np.percentile(np.abs(gradient_value), [gamma])).astype(int)
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X_new_group = X_new_group + np.multiply(eps * signed_grad, saliency_mat)
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adv_sample.append(X_new_group)
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'''print("Ground truth", teY[counter])
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print(model.predict(teX[counter].reshape(-1, 1000)))
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print(model.predict(X_new_group.reshape(-1,1000)))
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plt.plot(teX[counter])
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plt.show()
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plt.plot(X_new_group.reshape(-1,1),'r')
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plt.show()'''
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counter+=1
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adv_sample=np.array(adv_sample, dtype=float).reshape(-1,1000)
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score=model.evaluate(adv_sample, teY, verbose=0)
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print('Test loss:', score[0])
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print('Test accuracy:', score[1])
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teY_pred=np.argmax(model.predict(teX, batch_size=32), axis=1)
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adv_pred=np.argmax(model.predict(adv_sample, batch_size=32), axis=1)
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'''with open('test_true.csv', 'w') as f:
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writer = csv.writer(f)
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writer.writerows(np.argmax(teY, axis=1).reshape(-1, 1))
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with open('test_pred.csv', 'w') as f:
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writer = csv.writer(f)
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writer.writerows(teY_pred.reshape(-1, 1))
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with open('test_adversary.csv', 'w') as f:
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writer = csv.writer(f)
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writer.writerows(adv_pred.reshape(-1, 1))'''
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