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CNN_test/finetuning.py

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+# -*- coding: utf-8 -*-
+import os
+from keras.applications.vgg16 import VGG16
+from keras.layers import Input, Dropout, Flatten, Dense
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential, Model
+from keras import optimizers
+
+
+# CIFAR10のクラス一覧
+classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck']
+
+batch_size = 32
+nb_classes = len(classes)
+
+# CIFAR10の画像情報(x:32pic, y:32pic, channel:3)
+img_rows, img_cols = 32, 32
+channels = 3
+
+# 訓練用画像とテスト用画像のPath
+train_data_dir = '.\\cifar10\\train_image'
+validation_data_dir = '.\\cifar10\\test_image'
+
+# 訓練用画像(各クラス5000枚)、テスト用画像(各クラス1000枚)
+# epochは30くらいでも十分か？
+nb_train_samples = 50000
+nb_val_samples = 10000
+nb_epoch = 50
+
+# 学習済み重みの保存Path
+result_dir = '.\\cifar10\\results'
+if not os.path.exists(result_dir):
+    os.mkdir(result_dir)
+
+
+if __name__ == '__main__':
+    # VGG16モデルと学習済み重み(ImageNet)をロード
+    input_tensor = Input(shape=(img_rows, img_cols, 3))
+    vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)
+
+    # FC
+    top_model = Sequential()
+    top_model.add(Flatten(input_shape=vgg16.output_shape[1:]))
+    top_model.add(Dense(256, activation='relu'))
+    top_model.add(Dropout(0.5))
+    top_model.add(Dense(nb_classes, activation='softmax'))
+
+    # VGG16とFCを接続
+    model = Model(input=vgg16.input, output=top_model(vgg16.output))
+
+    # 最後の畳み込み層の手前までFreeze
+    for layer in model.layers[:15]:
+        layer.trainable = False
+
+    # 多クラス(=10)分類なのでloss=categorical_crossentropyとする
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
+                  metrics=['accuracy'])
+
+    train_datagen = ImageDataGenerator(
+        rescale=1.0 / 255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+    test_datagen = ImageDataGenerator(rescale=1.0 / 255)
+
+    train_generator = train_datagen.flow_from_directory(
+        train_data_dir,
+        target_size=(img_rows, img_cols),
+        color_mode='rgb',
+        classes=classes,
+        class_mode='categorical',
+        batch_size=batch_size,
+        shuffle=True)
+
+    validation_generator = test_datagen.flow_from_directory(
+        validation_data_dir,
+        target_size=(img_rows, img_cols),
+        color_mode='rgb',
+        classes=classes,
+        class_mode='categorical',
+        batch_size=batch_size,
+        shuffle=True)
+
+    # Fine-tuning
+    history = model.fit_generator(
+        train_generator,
+        samples_per_epoch=nb_train_samples,
+        nb_epoch=nb_epoch,
+        validation_data=validation_generator,
+        nb_val_samples=nb_val_samples)
+
+    model.save_weights(os.path.join(result_dir, 'finetuning.h5'))

CNN_test/predict.py

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+# -*- coding: utf-8 -*-
+import os
+import sys
+import numpy as np
+from keras.applications.vgg16 import VGG16
+from keras.models import Sequential, Model
+from keras.layers import Input, Dropout, Flatten, Dense
+from keras.preprocessing import image
+
+# CIFAR10のクラス一覧
+classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck']
+nb_classes = len(classes)
+
+# CIFAR10の画像情報(x:32pic, y:32pic, channel:3)
+img_height, img_width = 32, 32
+channels = 3
+
+# VGG16
+input_tensor = Input(shape=(img_height, img_width, channels))
+vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)
+
+# FC
+fc = Sequential()
+fc.add(Flatten(input_shape=vgg16.output_shape[1:]))
+fc.add(Dense(256, activation='relu'))
+fc.add(Dropout(0.5))
+fc.add(Dense(nb_classes, activation='softmax'))
+
+# VGG16とFCを接続
+model = Model(input=vgg16.input, output=fc(vgg16.output))
+
+# 学習済みの重み(finetuning.pyでFine-tuningした重み)をロード
+model.load_weights(os.path.join('cifar10\\results', 'finetuning.h5'))
+# model.load_weights(os.path.join('cifar10\\results', 'finetuning_noise.h5'))
+
+# 多クラス(=10)分類なのでloss=categorical_crossentropyとする
+model.compile(loss='categorical_crossentropy',
+              optimizer='adam',
+              metrics=['accuracy'])
+
+if sys.argv[1] == '':
+    print("usage: Test file is not found.")
+    sys.exit(1)
+
+# 画像を4次元テンソルに変換
+img = image.load_img(sys.argv[1], target_size=(img_height, img_width))
+x = image.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+x = x / 255.0
+
+# クラスの予測(top3を出力)
+pred = model.predict(x)[0]
+top = 3
+top_indices = pred.argsort()[-top:][::-1]
+result = [(classes[i], pred[i]) for i in top_indices]
+for x in result:
+    print(x)

CNN_test/search_adv.py

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+# -*- coding: utf-8 -*-
+import os
+import random
+import datetime
+import numpy as np
+import cv2
+from progressbar import ProgressBar
+from keras.applications.vgg16 import VGG16
+from keras.models import Sequential, Model
+from keras.layers import Input, Dropout, Flatten, Dense
+from keras.preprocessing import image
+
+# 検証対象のクラス
+CLASS = 'cat'
+TEST_DIR = 'cifar10\\test_image\\' + CLASS + '\\'
+ADV_DIR = 'results\\' + CLASS + '\\'
+TEST_IMAGES = 1000
+
+
+# ランダム選択したピクセルに細工を加える
+def random_adv(perturbation_filename, p):
+    # 画像読み込み
+    img = cv2.imread(TEST_DIR + perturbation_filename, cv2.IMREAD_UNCHANGED)
+
+    # 画像サイズの取得
+    if len(img) == 3:
+        height, width, channel = img.shape[:3]
+    else:
+        height, width = img.shape[:2]
+
+    for i in range(p):
+        # 細工するピクセルをランダムに選択
+        x = random.randint(0, width - 1)
+        y = random.randint(0, height - 1)
+
+        # ピクセル値の細工
+        pixel = img[y, x]
+        average = sum(pixel) / len(pixel)
+
+        if average < 128:
+            img[y, x] = [0, 0, 0]
+        else:
+            img[y, x] = [255, 255, 255]
+
+    # 細工画像の保存
+    adv_filename = 'adv_' + filename
+    cv2.imwrite(ADV_DIR + adv_filename, img)
+
+    return adv_filename
+
+
+# クラスの予測
+def predict(target_filename, height, width):
+    # 画像を4次元テンソルに変換
+    img = image.load_img(target_filename, target_size=(height, width))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = x / 255.0
+
+    # クラスの予測
+    pred = model.predict(x)[0]
+    top = 1
+    top_indices = pred.argsort()[-top:][::-1]
+    result = [(classes[i], pred[i]) for i in top_indices]
+
+    '''
+    result[0][0] : predict label
+    result[0][1] : prediction probability
+    '''
+    return result
+
+if __name__ == "__main__":
+    # CIFAR10のクラス一覧
+    classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+    nb_classes = len(classes)
+
+    # CIFAR10の画像情報(x:32pixel, y:32pixel, channel:3)
+    img_height, img_width = 32, 32
+    channels = 3
+
+    # VGG16
+    input_tensor = Input(shape=(img_height, img_width, channels))
+    vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)
+
+    # FC
+    fc = Sequential()
+    fc.add(Flatten(input_shape=vgg16.output_shape[1:]))
+    fc.add(Dense(256, activation='relu'))
+    fc.add(Dropout(0.5))
+    fc.add(Dense(nb_classes, activation='softmax'))
+
+    # VGG16とFCを接続
+    model = Model(input=vgg16.input, output=fc(vgg16.output))
+
+    # 学習済みの重み(finetuning.pyでFine-tuningした重み)をロード
+    model.load_weights(os.path.join('cifar10\\results', 'finetuning.h5'))
+    # model.load_weights(os.path.join('cifar10\\results', 'finetuning_noise.h5'))
+
+    # 多クラス(=10)分類なのでloss=categorical_crossentropyとする
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='adam',
+                  metrics=['accuracy'])
+
+    # 細工するピクセル数(p)、1画像たりの細工試行最大回数(p_max_num)
+    p = 1
+    p_max_num = 100
+
+    # 検証対象の画像一覧
+    test_files = os.listdir(TEST_DIR)
+
+    # 検証結果保存用のファイル
+    today_detail = datetime.datetime.today()
+    test_result = str(today_detail.strftime("%Y%m%d_%H%M%S")) + '.txt'
+    f = open(test_result, 'w')
+    f.write('Idx\tnormal image\tadversarial image\tmisclassify\tpredict\n')
+
+    # 細工処理
+    count = 0
+    progress_count = 1
+    progress = ProgressBar(min_value=1, max_value=TEST_IMAGES)
+    for filename in test_files:
+        # 進捗表示
+        progress.update(progress_count)
+        progress_count += 1
+
+        # 細工前画像のクラス分類予測
+        result = predict(TEST_DIR + filename, img_height, img_width)
+
+        # 正しく分類できない画像は検証しない
+        if CLASS != result[0][0]:
+            continue
+
+        for i in range(p_max_num):
+            p_filename = random_adv(filename, p)
+
+            # 細工画像のクラス分類予測
+            result = predict(ADV_DIR + p_filename, img_height, img_width)
+
+            # 誤分類した場合はログに書き込んで次の画像の検証を行う
+            if CLASS != result[0][0]:
+                count += 1
+                f.write(str(count)+'\t'+filename+'\t'+p_filename+'\t'+result[0][0]+'\t'+str(result[0][1])+'\n')
+                break
+    print('\n')
+    f.close()