Summary of A Hybrid Defense Strategy For Boosting Adversarial Robustness in Vision-language Models, by Yuhan Liang et al.
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models
by Yuhan Liang, Yijun Li, Yumeng Niu, Qianhe Shen, Hangyu Liu
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The proposed novel adversarial training framework integrates multiple attack strategies and advanced machine learning techniques to significantly enhance the robustness of Vision-Language Models (VLMs) against a broad range of adversarial attacks. The CLIP model achieved an accuracy of 43.5% on adversarially perturbed images, compared to only 4% for the baseline model. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate the effectiveness of the proposed method in enhancing model robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that Vision-Language Models are safe and reliable to use. These models can help with things like self-driving cars and medical diagnosis, but they can be tricked into giving wrong answers if someone makes them see fake images or text. Right now, there aren’t many good ways to make these models more secure, so the researchers in this paper came up with a new way to train them that makes them much less likely to get fooled. |
Keywords
» Artificial intelligence » Machine learning
