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Author: Richardhongyu

DFR.py

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+# from __future__ import absolute_import, division, print_function, unicode_literals
+# 导入TensorFlow和tf.keras
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import tensorflow as tf
+from tensorflow import keras
+from keras import initializers
+from keras import optimizers
+from keras.callbacks import *
+from keras.models import Sequential, load_model
+from keras.layers import Conv2D, LSTM, Flatten, Dense, Activation, BatchNormalization, Dropout, Reshape, MaxPooling2D
+from keras.callbacks import LearningRateScheduler, TensorBoard, ModelCheckpoint, ReduceLROnPlateau
+from keras.preprocessing.image import ImageDataGenerator
+from keras.regularizers import l1, l2
+from keras.utils import multi_gpu_model
+# 导入TensorFlow和tf.keras
+
+# 导入辅助库
+import numpy as np
+import matplotlib.pyplot as plt
+import datetime
+
+# 检验tensorflow版本
+print(tf.__version__)
+
+
+def mkdir(path):
+    # 引入模块
+    import os
+
+    # 去除首位空格
+    path = path.strip()
+    # 去除尾部 \ 符号
+    path = path.rstrip("\\")
+
+    # 判断路径是否存在
+    # 存在     True
+    # 不存在   False
+    isExists = os.path.exists(path)
+
+    # 判断结果
+    if not isExists:
+        # 如果不存在则创建目录
+        # 创建目录操作函数
+        os.makedirs(path)
+
+        print(path + ' 创建成功')
+        return True
+    else:
+        # 如果目录存在则不创建，并提示目录已存在
+        print(path + ' 目录已存在')
+        return False
+
+
+class ParallelModelCheckpoint(ModelCheckpoint):
+    def __init__(self, model, filepath, monitor='val_loss', verbose=0,
+                 save_best_only=False, save_weights_only=False,
+                 mode='auto', period=1):
+        self.single_model = model
+        super(ParallelModelCheckpoint, self).__init__(filepath, monitor, verbose, save_best_only, save_weights_only,
+                                                      mode, period)
+
+    def set_model(self, model):
+        super(ParallelModelCheckpoint, self).set_model(self.single_model)
+
+
+class LR_Updater(Callback):
+    '''This callback is utilized to log learning rates every iteration (batch cycle)
+    it is not meant to be directly used as a callback but extended by other callbacks
+    ie. LR_Cycle
+    '''
+
+    def __init__(self, iterations):
+        '''
+        iterations = dataset size / batch size
+        epochs = pass through full training dataset
+        '''
+
+        self.epoch_iterations = iterations
+        self.trn_iterations = 0.
+        self.history = {}
+
+    def on_train_begin(self, logs={}):
+        self.trn_iterations = 0.
+        logs = logs or {}
+
+    def on_batch_end(self, batch, logs=None):
+        logs = logs or {}
+        self.trn_iterations += 1
+        K.set_value(self.model.optimizer.lr, self.setRate())
+        self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))
+        self.history.setdefault('iterations', []).append(self.trn_iterations)
+        for k, v in logs.items():
+            self.history.setdefault(k, []).append(v)
+
+    def plot_lr(self):
+        plt.xlabel("iterations")
+        plt.ylabel("learning rate")
+        plt.plot(self.history['iterations'], self.history['lr'])
+
+    def plot(self, n_skip=10):
+        plt.xlabel("learning rate (log scale)")
+        plt.ylabel("loss")
+        plt.plot(self.history['lr'], self.history['loss'])
+        plt.xscale('log')
+
+
+class LR_Cycle(LR_Updater):
+    '''This callback is utilized to implement cyclical learning rates
+    it is based on this pytorch implementation https://github.com/fastai/fastai/blob/master/fastai
+    and adopted from this keras implementation https://github.com/bckenstler/CLR
+    '''
+
+    def __init__(self, iterations, cycle_mult=1):
+        '''
+        iterations = dataset size / batch size
+        iterations = number of iterations in one annealing cycle
+        cycle_mult = used to increase the cycle length cycle_mult times after every cycle
+        for example: cycle_mult = 2 doubles the length of the cycle at the end of each cy$
+        '''
+        self.min_lr = 0
+        self.cycle_mult = cycle_mult
+        self.cycle_iterations = 0.
+        super().__init__(iterations)
+
+    def setRate(self):
+        self.cycle_iterations += 1
+        if self.cycle_iterations == self.epoch_iterations:
+            print(self.epoch_iterations, 'change')
+            self.cycle_iterations = 0.
+            self.epoch_iterations *= self.cycle_mult
+        cos_out = np.cos(np.pi * (self.cycle_iterations) / self.epoch_iterations) + 1
+        if (self.cycle_iterations % 10) == 0:
+            print(self.max_lr / 2 * cos_out)
+        return self.max_lr / 2 * cos_out
+
+    def on_train_begin(self, logs={}):
+        super().on_train_begin(logs={})  # changed to {} to fix plots after going from 1 to mult. lr
+        self.cycle_iterations = 0.
+        self.max_lr = K.get_value(self.model.optimizer.lr)
+
+
+# HAPPY
+class DFR_model1:################################33
+    def __init__(self, epochs=100000, batch_size=512, load_weights=True):############################3 2 512
+        self.name = 'DFR_log'
+        self.model_filename = './DFR_log.h5'
+        self.num_classes = 2######################################################################3
+        self.input_shape = [28, 28, 1]
+        self.epochs = epochs  #
+        self.batch_size = batch_size  #
+        self.weight_decay = 0.0001
+        self.log_filepath = r'./DFR_log_tensorboard/'
+        self.conv_l1_regularizer = 0.00045  # # #################################### 0.00045 3
+        # self.lstm_l1_regularizer = 0.0003  #
+        self.start_lr = 0.001  ###############adam
+        self.end_lr = 0.0001  #bunengtaidi 0.0001
+        self.patience = 50  #50
+        self.epoch_1 = 1
+        self.epoch_2 = 2
+        self.epoch_3 = 3
+        self.lr_1 = 0.001
+        self.lr_2 = 0.001
+        self.lr_3 = 0.001  # 0.55  0.5 0.475 0.04625 0.45 0.   4375 0.4
+
+        if load_weights:
+            try:
+                self._model = load_model(self.model_filename)
+                print('Successfully loaded', self.name)
+            except (ImportError, ValueError, OSError) as e:
+                print(e)
+                print('Failed to load', self.name)
+
+    def count_params(self):
+        return self._model.count_params()
+
+    def build_model(self):
+        # self.batch_size = self.batch_size * strategy.num_replicas_in_sync
+        # with strategy.scope():
+        model = Sequential([
+
+            # # FLATTEN Finishedsparse_
+            Reshape((-1, 784, 1), input_shape=self.input_shape),
+
+            # # CONV 1 Finished
+            Conv2D(32, (1, 25,), padding='SAME', strides=[1, 1, ],
+                   kernel_initializer=initializers.random_normal(stddev=0.1),
+                   kernel_regularizer=l1(self.conv_l1_regularizer)),
+            # BatchNormalization(),
+            # Dropout(0.5),
+            Activation('relu'),
+            MaxPooling2D((1, 3), strides=(1, 3), padding='SAME'),
+
+            # # CONV 2 Finished
+            Conv2D(64, (1, 25,), padding='SAME', strides=[1, 1, ],
+                   kernel_initializer=initializers.random_normal(stddev=0.1),
+                   kernel_regularizer=l1(self.conv_l1_regularizer)),
+            # BatchNormalization(),
+            # Dropout(0.5),
+            Activation('relu'),
+            MaxPooling2D((1, 3), strides=(1, 3), padding='SAME'),
+
+            # # DENSE 1 / Dropout Finished
+            Flatten(),
+            Dense(1024, activation='relu', kernel_initializer=initializers.random_normal(stddev=0.1)),
+            BatchNormalization(),
+            # Dropout(0.2),###################################################### 1 0.5
+            Dense(2, activation='softmax', kernel_initializer=initializers.random_normal(stddev=0.1)),
+
+        ])
+        adam = optimizers.Adam(lr=self.start_lr, beta_1=0.9, beta_2=0.999, )  # 7.28增大10 times训练步长
+        model.compile(optimizer=adam,
+                      loss='categorical_crossentropy',
+                      metrics=['accuracy'])
+
+        # sparse_
+        return model
+
+    def scheduler(self, epoch):
+        # print(epoch, '--------------------------')
+        if epoch <= self.epoch_1:
+            return self.lr_1
+        if epoch <= self.epoch_2:
+            return self.lr_2
+        if epoch <= self.epoch_3:
+            return self.lr_3
+        return self.lr_3
+
+    def train(self):
+        train_data_path = './data_dfr_log/train_data.npy'
+        train_label_path = './data_dfr_log/train_label.npy'
+        test_data_path = './data_dfr_log/test_data.npy'
+        test_label_path = './data_dfr_log/test_label.npy'
+
+        train_data = np.load(train_data_path)
+        train_label = np.load(train_label_path)
+        test_data = np.load(test_data_path)
+        test_label = np.load(test_label_path)
+                                                                
+        print('train_data的数量为:', train_data.shape)
+        print('train_label的数量为:', train_label.shape)
+        print('test_data的数量为:', test_data.shape)
+        print('test_label的数量为:', test_label.shape)
+        
+        train_data = train_data.reshape([-1, 28, 28, 1])
+        # train_label = train_label.reshape([-1, 28, 28, 1])
+        test_data = test_data.reshape([-1, 28, 28, 1])
+        # test_label = test_label.reshape([-1, 28, 28, 1])
+
+        print('train_data的数量为:', train_data.shape)
+        print('train_label的数量为:', train_label.shape)
+        print('test_data的数量为:', test_data.shape)
+        print('test_label的数量为:', test_label.shape)
+
+        # 数据归一化到【0：255】
+        self.x_test = test_data.astype(int)
+        self.x_train = train_data.astype(int)
+        self.x_test = 2 * self.x_test
+        self.x_train = 2 * self.x_train
+        y_train = keras.utils.to_categorical(train_label, self.num_classes)
+        y_test = keras.utils.to_categorical(test_label, self.num_classes)
+        self.y_test = y_test.astype(int)
+        self.y_train = y_train.astype(int)
+
+        # 模型
+        model = self.build_model()
+        model.summary()
+
+        # 参数文件夹保存
+        mkdir(self.model_filename + 'date_' + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
+
+        # 训练
+        change_lr = LearningRateScheduler(self.scheduler)
+
+        checkpoint = ModelCheckpoint(
+            self.model_filename + 'date_' + datetime.datetime.now().strftime(
+                "%Y%m%d-%H%M%S") + '/' + 'epoch_' + '{epoch:02d}' + '_val_acc_' + '{val_acc:.4f}' + '.h5',
+            monitor='val_acc',
+            verbose=0,
+            save_best_only=True,
+            mode='auto',
+            period=5)
+        # plot_callback = PlotLearning()
+        tb_cb = TensorBoard(
+            log_dir=self.log_filepath + 'date_' + datetime.datetime.now().strftime(
+                "%Y%m%d-%H%M%S") + '_conv_l1_' + str(self.conv_l1_regularizer) + '_lstm_l1_' + str(
+                self.conv_l1_regularizer),
+            histogram_freq=0)
+
+        # lr change
+        reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.5, verbose=1,
+                                      patience=self.patience, min_lr=self.end_lr)
+
+        # SGDR_lr = LR_Cycle(5000, 2)
+        cbks = [checkpoint, tb_cb, reduce_lr]
+        print('Using real-time data augmentation.')
+        model.fit(x=self.x_train, y=self.y_train,
+                  batch_size=self.batch_size,
+                  epochs=self.epochs,
+                  callbacks=cbks,
+                  verbose=2,
+                  validation_data=(self.x_test, self.y_test),
+                  )
+        # save model
+        model.save(self.model_filename + '.h5')
+
+        self._model = model
+
+    def predict(self, img):
+        return self._model.predict(img, batch_size=self.batch_size)
+
+    def predict_one(self, img):
+        return self.predict(img)[0]
+
+    def accuracy(self):
+        return self._model.evaluate(self.x_test, self.y_test, verbose=0)[1]
+
+
+if __name__ == '__main__':
+    DFR = DFR_model1()
+    DFR.train()
+    print(DFR.accuracy())
+
+    # best(val_acc:97): 0。003 0。001 0.000001/
+    # goaled: 0.01 0.0005 0.0000001/0.003 0.003 0.000001/0。01 0。005 /0。003 0。001 0.000001/0.001 0.00013 0.0001
+    # failed: 0.01 0.001 0.000001/

DFR_log.py

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+# Attack code
+
+## The composition of the directoty
+
+- 0_AEEA_dataset
+  Our datasets are here including a log dataset,a traffic dataset.
+- 1_model_for_traffic
+  This directory includes our pretrained models in traffic dataset. You can get the training process by the tensorboard.
+- 2_model_for_log
+  This directory includes our pretrained models in traffic dataset. You can get the training process by the tensorboard.
+- 3_attack_code
+  You can get all the training code in this directory.
+  If you want to test your own model, you can add your model in the your_model_name.py and put your pretrained model here.
+  You can also try different ways to attck models, such as random attack,differential evolution.
+  It is convinient to try your models in different dataset.
+- 4_EVALUATION
+- 5_For_TEST_h5
+  You can get the accurate attack resutls.
+
+## How to attack your models
+
+- You need to pass your args to attack_for_traffic.py to attack models. EN:You need to try your own models before you attack it.
+- Example: python model_name.py --model model_name --others
+
+## Envirionment
+
+tensorflow_1_13_gpu 
+keras
+
+## Some important tips
+
+- You can write your own model that you want to attack in keras. And you need to follow the rules in the 3_attack_code/model_name.py. 
+- I write some comments for attacking models in the 3_attack_code/attack_for_traffic.py.
+- I write some comments for model building in the 3_attack_code/happy.py.
+- You can find the specific implements of differential_evolution in the 3_attack_code/differential_evolution.py.
+ 
+## GOOD LUCKY TO YOUR TRAVEL IN AI!