Summary of Scaling Laws For Black Box Adversarial Attacks, by Chuan Liu et al.
Scaling Laws for Black box Adversarial Attacks
by Chuan Liu, Huanran Chen, Yichi Zhang, Yinpeng Dong, Jun Zhu
First submitted to arxiv on: 25 Nov 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 The proposed work investigates the scaling laws of black-box adversarial attacks, aiming to improve the transferability of adversarial examples by increasing the number of surrogate models. The authors analyze the relationship between model ensemble size and attack success rates on various deep learning models, including image classifiers and large language models like GPT-4o. The results demonstrate a clear scaling effect, where using more surrogate models leads to improved adversarial transferability and higher attack success rates. This improvement is not limited to specific models or datasets, but rather applies consistently across different architectures. Furthermore, the authors provide insights into the interpretability of scaled attacks through visualization techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make artificial intelligence (AI) models more vulnerable to attacks. Right now, AI models are pretty good at recognizing pictures and understanding language. But someone could create a fake picture or sentence that would trick the model into making an incorrect decision. The researchers found that if you use many different AI models together, they can work together to make these fake examples even more effective. They tested this idea with lots of different AI models and found that it works pretty well. This is important because it helps us understand how we can protect our AI systems from being tricked by bad actors. |
Keywords
» Artificial intelligence » Deep learning » Gpt » Scaling laws » Transferability
