Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks

Anit Kumar Sahu, J. Zico Kolter, Satya Narayan Shukla

TL;DR: A query efficient one-step black-box adversarial attack

Abstract: We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations. We employ Markov Random Fields (MRF) to exploit the structure of input data to systematically model the covariance structure of the gradients. The MRF structure in addition to Bayesian inference for the gradients facilitates one-step attacks akin to Fast Gradient Sign Method (FGSM) albeit in the black-box setting. The resulting method uses fewer queries than the current state of the art to achieve comparable performance. In particular, in the regime of lower query budgets, we show that our method is particularly effective in terms of fewer average queries with high attack accuracy while employing one-step attacks.

Keywords: Black-Box Adversarial Attacks, Gaussian Markov Random Fields

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2010.04205/code)