attacks.py 文件源码

def attack(self, x, y):
        """
        This method creates a symbolic graph that given an input image,
        first randomly perturbs the image. The
        perturbation is bounded to an epsilon ball. Then multiple steps of
        gradient descent is performed to increase the probability of a target
        label or decrease the probability of the ground-truth label.

        :param x: A tensor with the input image.
        """
        import tensorflow as tf
        from cleverhans.utils_tf import clip_eta

        eta = tf.random_uniform(tf.shape(x), -self.eps, self.eps)
        eta = clip_eta(eta, self.ord, self.eps)

        for i in range(self.nb_iter):
            x, eta = self.attack_single_step(x, eta, y)

        adv_x = x + eta
        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)

        return adv_x