Summary of Anyattack: Targeted Adversarial Attacks on Vision-language Models Toward Any Images, by Jiaming Zhang et al.
AnyAttack: Targeted Adversarial Attacks on Vision-Language Models toward Any Images
by Jiaming Zhang, Junhong Ye, Xingjun Ma, Yige Li, Yunfan Yang, Jitao Sang, Dit-Yan Yeung
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Vision-Language Models (VLMs) have revolutionized real-world applications due to their multimodal capabilities. However, recent studies have revealed that VLMs are vulnerable to targeted adversarial attacks on images, which manipulate the model to generate harmful content specified by the adversary. The current attack methods rely on predefined target labels and lack scalability for large-scale robustness evaluations. This paper proposes AnyAttack, a self-supervised framework that generates targeted adversarial images without label supervision, allowing any image to serve as a target for the attack. Our framework employs pre-training and fine-tuning with the LAION-400M dataset. Extensive experiments on five mainstream VLMs across three multimodal tasks demonstrate the effectiveness of our attack. Additionally, we successfully transfer AnyAttack to multiple commercial VLMs. These results reveal an unprecedented risk to VLMs, highlighting the need for effective countermeasures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vision-Language Models are powerful tools that can understand and generate text based on images. Unfortunately, they can be tricked into creating harmful content if someone creates a special kind of fake image. Currently, these attacks require specific labels to work. This paper proposes a new way to create these attacks without needing those labels. The authors tested this method on several popular Vision-Language Models and found that it works well. They even tested it on commercial models used by companies like Google and Microsoft. This shows that these powerful tools are vulnerable to attacks, which is something we need to be aware of. |
Keywords
» Artificial intelligence » Fine tuning » Self supervised
