Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909613703561216 |
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| author | Zhang, Chiyu Zhou, Lu Xu, Xiaogang Wu, Jiafei Liu, Zhe |
| author_facet | Zhang, Chiyu Zhou, Lu Xu, Xiaogang Wu, Jiafei Liu, Zhe |
| contents | With the advent of Large Vision-Language Models (LVLMs), new attack vectors, such as cognitive bias, prompt injection, and jailbreaking, have emerged. Understanding these attacks promotes system robustness improvement and neural networks demystification. However, existing surveys often target attack taxonomy and lack in-depth analysis like 1) unified insights into adversariality, transferability, and generalization; 2) detailed evaluations framework; 3) motivation-driven attack categorizations; and 4) an integrated perspective on both traditional and LVLM attacks. This article addresses these gaps by offering a thorough summary of traditional and LVLM adversarial attacks, emphasizing their connections and distinctions, and providing actionable insights for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey Zhang, Chiyu Zhou, Lu Xu, Xiaogang Wu, Jiafei Liu, Zhe Computer Vision and Pattern Recognition Cryptography and Security With the advent of Large Vision-Language Models (LVLMs), new attack vectors, such as cognitive bias, prompt injection, and jailbreaking, have emerged. Understanding these attacks promotes system robustness improvement and neural networks demystification. However, existing surveys often target attack taxonomy and lack in-depth analysis like 1) unified insights into adversariality, transferability, and generalization; 2) detailed evaluations framework; 3) motivation-driven attack categorizations; and 4) an integrated perspective on both traditional and LVLM attacks. This article addresses these gaps by offering a thorough summary of traditional and LVLM adversarial attacks, emphasizing their connections and distinctions, and providing actionable insights for future research. |
| title | Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey |
| topic | Computer Vision and Pattern Recognition Cryptography and Security |
| url | https://arxiv.org/abs/2410.23687 |