人工智能在医疗领域的安全应用

    import tensorflow as tf
    from tensorflow.keras.applications.resnet50 import ResNet50
    from tensorflow.keras.preprocessing.image import ImageDataGenerator
    
    # 数据预处理
    train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)
    
    train_generator = train_datagen.flow_from_directory(
    'chest_xray/train',
    target_size=(224, 224))
    
    # 模型训练
    base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    x = base_model.output
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dense(512, activation='relu')(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    output = tf.keras.layers.Dense(14, activation='softmax')(x)
    
    model = tf.keras.Model(inputs=base_model.input, outputs=output)
    model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
    
    model.fit(train_generator, epochs=20, batch_size=32)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    import numpy as np
    
    # 生成对抗样本
    def fgsm(model, x, y, eps=0.1):
    with tf.GradientTape() as tape:
        tape.watch(x)
        pred = model(x)
        loss = tf.keras.losses.categorical_crossentropy(y, pred)
    grad = tape.gradient(loss, x)
    adv_x = x + eps * tf.sign(grad)
    return adv_x
    
    # 评估模型性能
    normal_acc = model.evaluate(test_generator)[1]
    adv_acc = model.evaluate(fgsm(model, test_generator.x, test_generator.y))[1]
    
    print(f'Normal accuracy: {normal_acc:.4f}')
    print(f'Adversarial accuracy: {adv_acc:.4f}')
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from tensorflow.keras.applications.resnet50 import ResNet50
    
    # 联邦学习框架
    class FederatedModel(tf.keras.Model):
    def __init__(self, base_model, num_clients):
        super(FederatedModel, self).__init__()
        self.base_model = base_model
        self.num_clients = num_clients
        self.client_models = [tf.keras.models.clone_model(base_model) for _ in range(num_clients)]
    
    def federated_train_step(self, client_data, client_idx):
        client_model = self.client_models[client_idx]
        with tf.GradientTape() as tape:
            pred = client_model(client_data[0])
            loss = tf.keras.losses.categorical_crossentropy(client_data[1], pred)
        grads = tape.gradient(loss, client_model.trainable_variables)
        client_model.optimizer.apply_gradients(zip(grads, client_model.trainable_variables))
        return loss
    
    def aggregate_weights(self):
        base_weights = self.base_model.get_weights()
        for client_model in self.client_models:
            client_weights = client_model.get_weights()
            base_weights = [bw * (self.num_clients - 1) / self.num_clients + cw / self.num_clients for bw, cw in zip(base_weights, client_weights)]
        self.base_model.set_weights(base_weights)
    
    # 模拟联邦学习过程
    base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    federated_model = FederatedModel(base_model, num_clients=5)
    
    for epoch in range(20):
    for client_idx in range(federated_model.num_clients):
        client_data = get_client_data(client_idx)
        federated_model.federated_train_step(client_data, client_idx)
    federated_model.aggregate_weights()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from tensorflow.keras.applications.resnet50 import ResNet50
    
    # 算法公平性训练
    class FairModel(tf.keras.Model):
    def __init__(self, base_model, sensitive_attr):
        super(FairModel, self).__init__()
        self.base_model = base_model
        self.sensitive_attr = sensitive_attr
        self.debias_model = tf.keras.Sequential([
            base_model.output_layer,
            tf.keras.layers.Dense(1, activation='sigmoid', name='sensitive_pred')
        ])
    
    def train_step(self, data):
        x, y, s = data
        with tf.GradientTape() as tape:
            base_pred = self.base_model(x)
            sensitive_pred = self.debias_model(x)
            base_loss = tf.keras.losses.categorical_crossentropy(y, base_pred)
            sensitive_loss = tf.keras.losses.binary_crossentropy(s, sensitive_pred)
            loss = base_loss - 0.1 * sensitive_loss
        grads = tape.gradient(loss, self.trainable_variables)
        self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
        return {'base_loss': base_loss, 'sensitive_loss': sensitive_loss}
    
    # 评估算法公平性
    base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    fair_model = FairModel(base_model, sensitive_attr='gender')
    
    train_data = get_train_data()
    fair_model.fit(train_data, epochs=20)
    
    test_data = get_test_data()
    base_acc, sensitive_acc = fair_model.evaluate(test_data)
    print(f'Base model accuracy: {base_acc:.4f}')
    print(f'Sensitive attribute accuracy: {sensitive_acc:.4f}')
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读