基于输入通道拆分的对抗攻击迁移性增强算法

doi:10.19678/j.issn.1000-3428.0064362

计算机工程 ›› 2023, Vol. 49 ›› Issue (1): 130-137. doi: 10.19678/j.issn.1000-3428.0064362

基于输入通道拆分的对抗攻击迁移性增强算法

郑德生¹, 陈继鑫¹, 周静¹, 柯武平¹, 陆超², 周永¹, 仇钎^2,3

1. 西南石油大学计算机科学学院, 成都 610500;
2. 中国航发四川燃气涡轮研究院航空发动机高空模拟技术重点实验室, 四川绵阳 621000;
3. 西北工业大学动力与能源学院, 西安 710072

收稿日期:2022-04-01 修回日期:2022-06-20 发布日期:2023-01-06
作者简介:郑德生(1983-),男,副研究员、博士,主研方向为网络空间安全、人工智能;陈继鑫(通信作者)、周静、柯武平,硕士研究生;陆超,副研究员;周永,副教授;仇钎,副研究员。
基金资助:
四川省科技计划重点研发项目“基于量子生成对抗网络的复杂图像处理关键技术研究”（2022YFG0315）；四川省科技计划重点研发项目“面向航空发动机多源试验数据智能编目与推理融合的研究与应用”（2022YFG0174）；中国航空发动机集团有限公司四川燃气涡轮研究院稳定支持项目（GJCZ-2019-59）；成都市重点示范项目“基于B2T的智能物流云平台的建设及应用示范项目”（2019-YF09-00044-CG）。

Adversarial Attack Transferability Enhancement Algorithm Based on Input Channel Splitting

ZHENG Desheng¹, CHEN Jixin¹, ZHOU Jing¹, KE Wuping¹, LU Chao², ZHOU Yong¹, QIU Qian^2,3

1. School of Computer Science, Southwest Petroleum University, Chengdu 610500, China;
2. Key Laboratory on Aero-Engine Altitude Simulation Technology, Sichuan Gas Turbine Establishment, AECC, Mianyang, Sichuan 621000, China;
3. School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072, China

Received:2022-04-01 Revised:2022-06-20 Published:2023-01-06

摘要/Abstract

摘要： 深度神经网络已被应用于人脸识别、自动驾驶等场景中，但容易受到对抗样本的攻击。对抗样本的生成方法被分为白盒攻击和黑盒攻击，当对抗攻击算法攻击白盒模型时存在过拟合问题，导致生成对抗样本的迁移性降低。提出一种用于生成高迁移性对抗样本的对抗攻击算法CSA。在每次迭代过程中，通过对输入RGB图片的通道进行拆分，得到三张具有一个通道的输入图片，并对其进行零值填充，获得三张具有三个通道的输入图片。将最终得到的图片与原始RGB输入图片共同传入到模型中进行梯度计算，调整原始梯度的更新方向，避免出现局部最优。在此基础上，通过符号法生成对抗样本。在ImageNet数据集上的实验验证该算法的有效性，结果表明，CSA算法能够有效提高对抗攻击的迁移性，在四种常规训练模型上的攻击成功率平均为84.2%，与DIM、TIM结合所得DI-TI-CSA算法在三种对抗训练黑盒模型上的攻击成功率平均为94.7%，对七种防御模型的攻击成功率平均为91.8%。

关键词: 对抗攻击, 迁移性增强, 对抗样本, 白盒模型, ImageNet数据集

Abstract: The Deep Neural Network(DNN) has been widely used in face recognition, automatic driving, and other scenarios;however, it is vulnerable to attacks by adversarial samples.Methods by which adversarial samples are generated can be classified into white-box and black-box attacks.When the adversarial attack algorithm attacks the white-box model, overfitting occurs, which reduces the transferability of the generated adversarial samples.Herein, an adversarial attack algorithm CSA is proposed to generate high transferability adversarial samples.During the iteration of each attack, three input pictures with one channel are obtained by splitting the channels of the input RGB pictures, and zero filling is performed to obtain three input pictures with three channels.The final image and the original RGB input image are transferred to the model for gradient calculation, and the update direction of the original gradient is adjusted to avoid local optimization.Subsequently, adversarial samples are generated symbolically.An experiment performed on the ImageNet dataset verifies the effectiveness of the proposed algorithm.In particular, the results show that the proposed algorithm can effectively improve the transferability of adversarial attacks.The average attack success rate on four conventional training models is 84.2%, whereas the DI-TI-CSA algorithm based on the combination of CSA, DIM and TIM is 94.7% on three adversarial training black-box models and 91.8% on seven defense models.

Key words: adversarial attack, transferability enhancement, adversarial sample, white-box model, ImageNet dataset

中图分类号:

TP391

郑德生, 陈继鑫, 周静, 柯武平, 陆超, 周永, 仇钎. 基于输入通道拆分的对抗攻击迁移性增强算法[J]. 计算机工程, 2023, 49(1): 130-137.

ZHENG Desheng, CHEN Jixin, ZHOU Jing, KE Wuping, LU Chao, ZHOU Yong, QIU Qian. Adversarial Attack Transferability Enhancement Algorithm Based on Input Channel Splitting[J]. Computer Engineering, 2023, 49(1): 130-137.

https://www.ecice06.com/CN/Y2023/V49/I1/130

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