我正在尝试编写自己的损失函数来创建图像的对抗性示例，尤其是面孔。该损失函数的一个组成部分基于图像嵌入的余弦距离，而另一种嵌入是静态的。嵌入是Facenet https://github.com/davidsandberg/facenet模型的结果。我知道如何获得此嵌入。我面临的问题是，使用ADAM优化器将损失降到最低时，用于嵌入的损失分量与模型正在学习的新图像之间没有梯度。它会重新创建原始输入图像，但不会修改特定区域以更改生成的嵌入（嵌入和生成的图像之间没有梯度，只有生成的图像和起始图像之间没有渐变）。有没有一种方法可以基于Facenet模型的结果来创建损耗分量？以下代码基于https://github.com/carlini/nn_robust_attacks。
# the variable we're going to optimize over
self.modifier = tf.Variable(np.zeros(shape, dtype=np.float32))
# these are variables to be more efficient in sending data to tf
self.timg = tf.Variable(np.zeros(shape), dtype=tf.float32)
# self.tlab = tf.Variable(np.zeros((batch_size, num_labels)), dtype=tf.float32)
self.const = tf.Variable(np.zeros(batch_size), dtype=tf.float32)
# and here's what we use to assign them
self.assign_timg = tf.placeholder(tf.float32, shape)
# self.assign_tlab = tf.placeholder(tf.float32, (batch_size, num_labels))
self.assign_const = tf.placeholder(tf.float32, [batch_size])
self.boxmul = (boxmax - boxmin) / 2.
self.boxplus = (boxmin + boxmax) / 2.
self.nimg = self.modifier + self.timg
self.newimg = tf.Variable(tf.tanh(self.nimg) * self.boxmul + self.boxplus)
start_image = np.tanh(starting_image/255 ) * self.boxmul + self.boxplus
mask = np.ones((160, 160, 3), dtype=np.float32)
y_offset = msk.y_offset()
x_offset = msk.x_offset()
mask_indices = msk.return_mask()
for idx, (j, i) in enumerate(mask_indices):
mask[i + y_offset, j + x_offset, 0] = 0
mask[i + y_offset, j + x_offset, 1] = 0
mask[i + y_offset, j + x_offset, 2] = 0
mask_tensor = tf.convert_to_tensor(mask)
# get the difference between the starting image (masked) and the perturbation applied on top of the masked image
self.img_variation = self.newimg[0] - start_image
# self.img_variation = tf.Variable(diff_img)
# get the difference in images (values bewteen pixels, masked areas are set to 0 so they don't count)
self.img_loss = tf.reduce_sum(tf.square(tf.multiply(self.img_variation, mask_tensor)))
self.embedding_output = tf.get_default_graph().get_tensor_by_name("embeddings:0")
self.face_input = tf.get_default_graph().get_tensor_by_name("input:0")
# get centroid
centroid_embedding = auth.getCentroid(centroid_path)
normalize_embedding = tf.nn.l2_normalize(self.embedding_output, 1)
normalize_centroid = tf.nn.l2_normalize(centroid_embedding, 1)
# get the cosine distance of the centroid to the calculated embedding
self.cos_dist = tf.losses.cosine_distance(normalize_embedding, normalize_centroid, axis=1)
# loss component for update region for perturbed image
self.loss_update = tf.reduce_sum(abs(self.update_threshold + 0.01 - self.cos_dist))
# total loss
self.loss = self.loss_update + self.img_loss
# Setup the adam optimizer and keep track of variables we're creating
start_vars = set(x.name for x in tf.global_variables())
optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE)
self.train = optimizer.minimize(self.loss, var_list=[self.newimg])
end_vars = tf.global_variables()
new_vars = [x for x in end_vars if x.name not in start_vars]
# these are the variables to initialize when we run
self.setup = []
self.setup.append(self.timg.assign(self.assign_timg))
# self.setup.append(self.tlab.assign(self.assign_tlab))
self.setup.append(self.const.assign(self.assign_const))
self.init = tf.variables_initializer(var_list=[self.newimg] + new_vars)
self.init = tf.variables_initializer(var_list=[self.newimg])
self.init = tf.global_variables_initializer()
然后是实际训练
self.sess.run(self.setup, {self.assign_timg: [batch],
# self.assign_tlab: batchlab,
self.assign_const: CONST})
prev = np.inf
for iteration in range(self.MAX_ITERATIONS):
print(iteration)
# perform the attack
img = np.round(np.arctanh((o_bestattack - self.boxplus) / self.boxmul * 0.999999) * 255)
imgs = np.zeros((1, 160, 160, 3), np.float32)
imgs[0] = img
batch_size = tf.get_default_graph().get_tensor_by_name("batch_size:0")
feed_dict = {self.face_input: imgs,
batch_size: 1,
phase_train_placeholder: False}
_, l, scores, newimg = self.sess.run([self.train, self.loss,
self.embedding_output, self.newimg], feed_dict=feed_dict)