Summary of Gadt: Enhancing Transferable Adversarial Attacks Through Gradient-guided Adversarial Data Transformation, by Yating Ma and Xiaogang Xu and Liming Fang and Zhe Liu
GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformation
by Yating Ma, Xiaogang Xu, Liming Fang, Zhe Liu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper proposes a novel Data Augmentation (DA)-based attack algorithm called GADT. The goal is to optimize DA parameters along with Adversarial Noise (AN) to generate more effective transferable adversarial examples (TAEs). Existing DA-based strategies often struggle to find optimal solutions due to the challenging DA search procedure without proper guidance. To address this, GADT iteratively updates AN based on posterior estimates and employs a differentiable DA operation library to identify adversarial DA parameters. The algorithm also introduces a new loss function that enhances adversarial effects while preserving original image content. Experimental results demonstrate that GADT can be integrated with existing transferable attack methods, updating their DA parameters effectively while retaining their AN formulation strategies. This work has implications for real-world AI applications in both research and industrial contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer systems smarter by creating fake images that trick them into making bad decisions. Right now, people are mostly generating these fake images by adding noise to normal pictures. But researchers think it’s better to mix up the data used to train the system, so they developed a new way to do this called GADT. This method tries different combinations of data mixing and noise addition until it finds the combination that works best at fooling the system. The results show that GADT is effective in making systems more vulnerable to fake images. |
Keywords
» Artificial intelligence » Data augmentation » Loss function
