publications

2025

1. CVPR

   MOS-Attack: A Scalable Multi-Objective Adversarial Attack Framework

   Ping Guo, Cheng Gong, Xi Lin, and 4 more authors

   In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2025

   Abs DOI Bib HTML Code

   

   Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-differentiable 0-1 loss function. However, existing single objective methods, namely adversarial attacks focus on a surrogate loss function, do not fully harness the benefits of engaging multiple loss functions, as a result of insufficient understanding of their synergistic and conflicting nature. To overcome these limitations, we propose the Multi-Objective Set-Based Attack (MOS Attack), a novel adversarial attack framework leveraging multiple loss functions and automatically uncovering their interrelations. The MOS Attack adopts a set-based multi-objective optimization strategy, enabling the incorporation of numerous loss functions without additional parameters. It also automatically mines synergistic patterns among various losses, facilitating the generation of potent adversarial attacks with fewer objectives. Extensive experiments have shown that our MOS Attack outperforms single-objective attacks. Furthermore, by harnessing the identified synergistic patterns, MOS Attack continues to show superior results with a reduced number of loss functions.

   @inproceedings{guo2025mos,
     address = {Los Alamitos, CA, USA},
     author = {Guo, Ping and Gong, Cheng and Lin, Xi and Liu, Fei and Lu, Zhichao and Zhang, Qingfu and Wang, Zhenkun},
     booktitle = {2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     dimensions = {true},
     doi = {10.1109/CVPR52734.2025.00475},
     keywords = {Computer vision, Codes, Artificial neural networks, Robustness, Adversarial machine learning, Pattern recognition, Optimization},
     month = jun,
     pages = {5041-5051},
     publisher = {IEEE Computer Society},
     title = {MOS-Attack: A Scalable Multi-Objective Adversarial Attack Framework},
     year = {2025}
   }