Summary of Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds, By Shuhai Zhang et al.
Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds
by Shuhai Zhang, Jiahao Yang, Hui Luo, Jie Chen, Li Wang, Feng Liu, Bo Han, Mingkui Tan
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A novel approach to improving the robustness of deep neural networks (DNNs) is presented in this paper, which focuses on the clean data manifold instead of traditional adversarial purification methods. The proposed Consistency Model-based Adversarial Purification (CMAP) leverages a well-trained generative model to generate samples that are close to clean ones but far from adversarial ones. CMAP optimizes vectors within the latent space of a pre-trained consistency model to restore clean data, ensuring robustness against strong adversarial attacks while preserving high natural accuracy. The paper’s key contributions include a perceptual consistency restoration mechanism, latent distribution consistency constraint strategy, and latent vector consistency prediction scheme. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers find a way to make deep neural networks more reliable by looking at the data in a different way. Instead of trying to get rid of bad information, they generate new good information that’s close to what the network is used to seeing. This helps keep the network from making mistakes when it sees fake or “adversarial” data. The method, called CMAP, works well on big datasets like ImageNet and keeps the network accurate even when faced with strong attacks. |
Keywords
» Artificial intelligence » Generative model » Latent space
