BlackboxBench: A Comprehensive Benchmark of Black-Box Adversarial Attacks
Meixi Zheng, Xuanchen Yan, Zihao Zhu, Hongrui Chen, Baoyuan Wu
Abstract
Adversarial examples are well-known tools to evaluate the vulnerability of deep neural networks (DNNs). Although lots of adversarial attack algorithms have been developed, it's still challenging in the practical scenario that the model's parameters and architectures are inaccessible to the attacker/evaluator, i.e., black-box adversarial attacks. Due to the practical importance, there has been rapid progress from recent algorithms, reflected by the quick increase in attack success rate and quick decrease in query numbers to the target model. However, there lacks thorough evaluations and comparisons among these algorithms, causing difficulties in tracking the real progress, analyzing advantages and disadvantages of different technical routes, as well as designing future development roadmap of this field. Thus, we aim at building a comprehensive benchmark of black-box adversarial attacks, called BlackboxBench. It mainly provides: 1) a unified, extensible and modular-based codebase, implementing 29 query-based attack algorithms and 30 transfer-based attack algorithms; 2) comprehensive evaluations: we evaluate the implemented algorithms against several mainstreaming model architectures on 2 widely used datasets (CIFAR-10 and a subset of ImageNet), leading to 14,950 evaluations$^{1}$1 in total; 3) thorough analysis and new insights, as well analytical tools.