Hui Xia (Ocean University of China), Rui Zhang (Ocean University of China), Zi Kang (Ocean University of China), Shuliang Jiang (Ocean University of China), Shuo Xu (Ocean University of China)

Although there has been extensive research on the transferability of adversarial attacks, existing methods for generating adversarial examples suffer from two significant drawbacks: poor stealthiness and low attack efficacy under low-round attacks. To address the above issues, we creatively propose an adversarial example generation method that ensembles the class activation maps of multiple models, called class activation mapping ensemble attack. We first use the class activation mapping method to discover the relationship between the decision of the Deep Neural Network and the image region. Then we calculate the class activation score for each pixel and use it as the weight for perturbation to enhance the stealthiness of adversarial examples and improve attack performance under low attack rounds. In the optimization process, we also ensemble class activation maps of multiple models to ensure the transferability of the adversarial attack algorithm. Experimental results show that our method generates adversarial examples with high perceptibility, transferability, attack performance under low-round attacks, and evasiveness. Specifically, when our attack capability is comparable to the most potent attack (VMIFGSM), our perceptibility is close to the best-performing attack (TPGD). For non-targeted attacks, our method outperforms the VMIFGSM by an average of 11.69% in attack capability against 13 target models and outperforms the TPGD by an average of 37.15%. For targeted attacks, our method achieves the fastest convergence, the most potent attack efficacy, and significantly outperforms the eight baseline methods in low-round attacks. Furthermore, our method can evade defenses and be used to assess the robustness of models.

View More Papers

DorPatch: Distributed and Occlusion-Robust Adversarial Patch to Evade Certifiable...

Chaoxiang He (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Bin B. Zhu (Microsoft Research), Yimiao Zeng (Huazhong University of Science and Technology), Hanqing Hu (Huazhong University of Science and Technology), Xiaofan Bai (Huazhong University of Science and Technology), Hai Jin (Huazhong University of Science and Technology), Dongmei Zhang…

Read More

File Hijacking Vulnerability: The Elephant in the Room

Chendong Yu (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Yang Xiao (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Jie Lu (Institute of Computing Technology of the Chinese Academy of Sciences), Yuekang…

Read More

ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

Read More

SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

Read More