attacks.py 文件源码

def attack_single_step(self, x, eta, y):
        """
        Given the original image and the perturbation computed so far, computes
        a new perturbation.

        :param x: A tensor with the original input.
        :param eta: A tensor the same shape as x that holds the perturbation.
        :param y: A tensor with the target labels or ground-truth labels.
        """
        import tensorflow as tf
        from cleverhans.utils_tf import model_loss, clip_eta

        adv_x = x + eta
        preds = self.model.get_probs(adv_x)
        loss = model_loss(y, preds)
        if self.targeted:
            loss = -loss
        grad, = tf.gradients(loss, adv_x)
        scaled_signed_grad = self.eps_iter * tf.sign(grad)
        adv_x = adv_x + scaled_signed_grad
        if self.clip_min is not None and self.clip_max is not None:
            adv_x = tf.clip_by_value(adv_x, self.clip_min, self.clip_max)
        eta = adv_x - x
        eta = clip_eta(eta, self.ord, self.eps)
        return x, eta