Distillation-Enhanced Physical Adversarial Attacks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915090533449728 |
|---|---|
| author | Liu, Wei Wu, Yonglin Li, Chaoqun Liu, Zhuodong Yan, Huanqian |
| author_facet | Liu, Wei Wu, Yonglin Li, Chaoqun Liu, Zhuodong Yan, Huanqian |
| contents | The study of physical adversarial patches is crucial for identifying vulnerabilities in AI-based recognition systems and developing more robust deep learning models. While recent research has focused on improving patch stealthiness for greater practical applicability, achieving an effective balance between stealth and attack performance remains a significant challenge. To address this issue, we propose a novel physical adversarial attack method that leverages knowledge distillation. Specifically, we first define a stealthy color space tailored to the target environment to ensure smooth blending. Then, we optimize an adversarial patch in an unconstrained color space, which serves as the 'teacher' patch. Finally, we use an adversarial knowledge distillation module to transfer the teacher patch's knowledge to the 'student' patch, guiding the optimization of the stealthy patch. Experimental results show that our approach improves attack performance by 20%, while maintaining stealth, highlighting its practical value. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distillation-Enhanced Physical Adversarial Attacks Liu, Wei Wu, Yonglin Li, Chaoqun Liu, Zhuodong Yan, Huanqian Computer Vision and Pattern Recognition The study of physical adversarial patches is crucial for identifying vulnerabilities in AI-based recognition systems and developing more robust deep learning models. While recent research has focused on improving patch stealthiness for greater practical applicability, achieving an effective balance between stealth and attack performance remains a significant challenge. To address this issue, we propose a novel physical adversarial attack method that leverages knowledge distillation. Specifically, we first define a stealthy color space tailored to the target environment to ensure smooth blending. Then, we optimize an adversarial patch in an unconstrained color space, which serves as the 'teacher' patch. Finally, we use an adversarial knowledge distillation module to transfer the teacher patch's knowledge to the 'student' patch, guiding the optimization of the stealthy patch. Experimental results show that our approach improves attack performance by 20%, while maintaining stealth, highlighting its practical value. |
| title | Distillation-Enhanced Physical Adversarial Attacks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.02232 |