Deep learning is widely used in classification tasks to achieve advanced performance. However, in the face of well-designed image classifications, such as the Fast Gradient Sign Method (FGSM), there are glaring errors. This paper proposes a new technique to eliminate interference using generative adversarial networks (GAN), called multi-branch perturbed generative adversarial networks(MBP-GAN). MBP-GAN minimizes the input feature flow graph in generator noise filtering by introducing multi-branch fusion perturbations. This makes the sample of the generator more aware of this perturbation, thereby improving the ability of the generator and discriminator to resist classification against attacks in combat training. Through this kind of training, this model can be used as a defense against arbitrary attacks. Then we design the loss function, so that the generator and the discriminator can keep accurate results for general images and classification against images. We verify our experimental results on the MNIST, F-MNIST and CelebA datasets. The results show that the MBP-GAN can effectively eliminate the interference from the classification against the attack.

深度学习广泛用于分类任务中，以实现高级性能。但是，面对设计良好的图像分类，例如快速渐变符号方法（FGSM），存在明显的错误。本文提出了一种使用生成对抗网络（GAN）消除干扰的新技术，称为多分支摄动生成对抗网络（MBP-GAN）。MBP-GAN通过引入多分支融合扰动来最大程度地减少发电机噪声过滤中的输入特征流图。这使生成器的样本更加意识到这种扰动，从而提高了生成器和区分器在战斗训练中抵抗分类以抵抗攻击的能力。通过这种训练，该模型可以用作针对任意攻击的防御。然后我们设计损失函数，以便生成器和鉴别器可以为普通图像和针对图像的分类保持准确的结果。我们在MNIST，F-MNIST和CelebA数据集上验证了我们的实验结果。结果表明，MBP-GAN可以有效地消除分类对攻击的干扰。