Summary of Your Diffusion Model Is Secretly a Certifiably Robust Classifier, by Huanran Chen et al.
Your Diffusion Model is Secretly a Certifiably Robust Classifier
by Huanran Chen, Yinpeng Dong, Shitong Shao, Zhongkai Hao, Xiao Yang, Hang Su, Jun Zhu
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Generative learning has shown promise in modeling data distributions and handling out-of-distribution instances, particularly in enhancing robustness to adversarial attacks. Diffusion classifiers have demonstrated superior empirical robustness by leveraging powerful diffusion models. However, a comprehensive theoretical understanding of their robustness is still lacking, raising concerns about their vulnerability to stronger future attacks. This study proves that diffusion classifiers possess O(1) Lipschitzness and establish certified robustness, demonstrating inherent resilience. To achieve non-constant Lipschitzness and tighter certified robustness, the authors generalize diffusion classifiers to classify Gaussian-corrupted data by deriving evidence lower bounds (ELBOs), approximating likelihood using ELBO, and calculating classification probabilities via Bayes’ theorem. Experimental results show superior certified robustness of Noised Diffusion Classifiers (NDCs) on CIFAR-10 under adversarial perturbations with l2 norms less than 0.25 and 0.5, respectively, using a single off-the-shelf diffusion model without additional data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make machines better at recognizing things even when they’re not quite like the ones they’ve seen before. Right now, we have ways to make machines more robust, but we don’t really understand why they work or if they’ll be good enough for the future. The researchers looked at a type of machine learning called diffusion classifiers and found that they can be very good at recognizing things even when they’re changed slightly. They also showed how to make these machines even better by using something called evidence lower bounds (ELBOs). This means we can trust that these machines will do a great job in the future, even if things get a little weird. |
Keywords
* Artificial intelligence * Classification * Diffusion * Diffusion model * Likelihood * Machine learning
