attack_carlini_wagner_l2.py 文件源码

def _optimize(self, optimizer, model, input_var, modifier_var, target_var, scale_const_var, input_orig=None):
        # apply modifier and clamp resulting image to keep bounded from clip_min to clip_max
        if self.clamp_fn == 'tanh':
            input_adv = tanh_rescale(modifier_var + input_var, self.clip_min, self.clip_max)
        else:
            input_adv = torch.clamp(modifier_var + input_var, self.clip_min, self.clip_max)

        output = model(input_adv)

        # distance to the original input data
        if input_orig is None:
            dist = l2_dist(input_adv, input_var, keepdim=False)
        else:
            dist = l2_dist(input_adv, input_orig, keepdim=False)

        loss = self._loss(output, target_var, dist, scale_const_var)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        loss_np = loss.data[0]
        dist_np = dist.data.cpu().numpy()
        output_np = output.data.cpu().numpy()
        input_adv_np = input_adv.data.permute(0, 2, 3, 1).cpu().numpy()  # back to BHWC for numpy consumption
        return loss_np, dist_np, output_np, input_adv_np