Summary of Random Sampling For Diffusion-based Adversarial Purification, by Jiancheng Zhang et al.
Random Sampling for Diffusion-based Adversarial Purification
by Jiancheng Zhang, Peiran Dong, Yongyong Chen, Yin-Ping Zhao, Song Guo
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A new approach to adversarial purification is introduced through an opposite sampling scheme called random sampling, which shows promise in improving the robustness of Denoising Diffusion Probabilistic Models (DDPMs) against attacks. Building upon the stability-focused Denoising Diffusion Implicit Model (DDIM), random sampling injects more randomness into each diffusion process, leading to stronger resilience against adversarial threats. To guarantee consistent predictions under purified and clean image inputs, a novel mediator conditional guidance is proposed. Experimental evaluations demonstrate significant performance improvements in multiple settings, with the proposed method achieving an impressive 20% robustness advantage over state-of-the-art approaches while maintaining 10-fold acceleration. The introduced DiffAP baseline outperforms existing methods in both defensive stability and purification efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make computer models more resistant to attacks from hackers. They created a new technique called random sampling, which makes the model less predictable and harder to manipulate. This is important because many models are vulnerable to these kinds of attacks, which can cause them to produce false or misleading results. The new method works by adding noise to the data being processed, making it harder for attackers to understand how the model is working. In tests, this approach was shown to be much better at defending against attacks than existing methods. |
Keywords
» Artificial intelligence » Diffusion
