Summary of Efficient Black-box Adversarial Attacks Via Bayesian Optimization Guided by a Function Prior, By Shuyu Cheng et al.
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
by Shuyu Cheng, Yibo Miao, Yinpeng Dong, Xiao Yang, Xiao-Shan Gao, Jun Zhu
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 Prior-guided Bayesian Optimization (P-BO), an algorithm that leverages a surrogate white-box model as a global function prior in black-box adversarial attacks. P-BO models the attack objective with a Gaussian process, initialized with the surrogate model’s loss, to generate efficient and effective adversarial examples against black-box models. Theoretical analysis reveals that the performance of P-BO can be affected by a bad prior, so an adaptive integration strategy is introduced to adjust a coefficient on the function prior to minimize regret. Experimental results demonstrate the superiority of P-BO in reducing queries and improving attack success rates compared to state-of-the-art black-box attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary P-BO is a new way to make computers do what we want, even if we don’t know how they work. It’s like having a secret map that helps us find the right path. This paper shows how P-BO can be used to trick computers into doing things they wouldn’t normally do, and it’s much better at doing this than other ways that people have tried. |
Keywords
» Artificial intelligence » Optimization
