基于深度学习的图像攻击检测中的准确度问题

import tensorflow as tf
from tensorflow.keras import layers

# 构建卷积神经网络模型
def cnn_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.Flatten())
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Dense(10))
    return model

# 数据增强
data_augmentation = tf.keras.Sequential([
  layers.experimental.preprocessing.Rescaling(1./255),
  layers.experimental.preprocessing.RandomRotation(0.1),
  layers.experimental.preprocessing.RandomZoom(0.1),
])

# 引入先验知识
def prior_knowledge_loss(y_true, y_pred):
    loss = ...
    return loss

# 构建图像攻击检测模型
def attack_detection_model():
    base_model = cnn_model()
    inp = layers.Input(shape=(28, 28, 1))
    x = data_augmentation(inp)
    features = base_model(x)
    predictions = layers.Dense(1, activation='sigmoid')(features)
    model = tf.keras.Model(inputs=inp, outputs=predictions)
    model.compile(optimizer='adam', loss=[prior_knowledge_loss, 'binary_crossentropy'])
    return model

# 训练模型
model = attack_detection_model()
model.fit(train_dataset, epochs=10, validation_data=val_dataset)

# 测试模型
loss, accuracy = model.evaluate(test_dataset)
print('Test accuracy:', accuracy)