adver.py 文件源码

def iterate(self, x, eps=32, alp=1.0):
        num_iter = min(eps + 4, 1.25 * eps)
        loss = 1.0
        x = np.copy(x)
        while loss > 0 and num_iter > 0:
            inp = x.reshape((1,) + inp_size)
            outs = self.f_outputs([inp, 0])
            loss = outs[0]
            print('Loss: ', loss)
            grads = np.array(outs[1:]).reshape(inp_size)
            s_grads = np.sign(grads)
            adv_x = x - alp * s_grads
            sub_x = np.minimum(x + eps, np.maximum(x - eps, adv_x))
            next_x = preprocess_img(np.clip(deprocess_img(sub_x), 0.0, 255.0))
            x = next_x
            confidence = self.mdl.predict(x.reshape((1,) + inp_size))[0][0]
            print('Current confidence value: ', confidence) #'minval =', min_val)
            yield (deprocess_img(x), confidence)
            num_iter -= 1