Black-Box Adversarial Attacks | Security Research Group

SaTML Talk: SoK: Pitfalls in Evaluating Black-Box Attacks

22 April 2024 Fnu Suya, Anshuman Suri, Tingwei Zhang, Jingtao Hong, Yuan Tian, David Evans, SaTML, adversarial machine learning, black-box adversarial attacks, systemization of knowledge

Anshuman Suri’s talk at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) is now available:

See the earlier blog post for more on the work, and the paper at https://arxiv.org/abs/2310.17534.

SoK: Pitfalls in Evaluating Black-Box Attacks

20 December 2023 Fnu Suya, Anshuman Suri, Tingwei Zhang, Jingtao Hong, Yuan Tian, David Evans, SaTML, adversarial machine learning, black-box adversarial attacks, systemization of knowledge

Post by Anshuman Suri and Fnu Suya Much research has studied black-box attacks on image classifiers, where adversaries generate adversarial examples against unknown target models without having access to their internal information. Our analysis of over 164 attacks (published in 102 major security, machine learning and security conferences) shows how these works make different assumptions about the adversary’s knowledge. The current literature lacks cohesive organization centered around the threat model. Our SoK paper (to appear at IEEE SaTML 2024) introduces a taxonomy for systematizing these attacks and demonstrates the importance of careful evaluations that consider adversary resources and threat models.