Semantic-Aware Adaptive Binary Search for Hard-Label Black-Box Attack

Yiqing Ma, Kyle Lucke, Min Xian, Aleksandar Vakanski

Abstract: Despite the widely reported potential of deep neural networks for automated breast tumor classification and detection, these models are vulnerable to adversarial attacks, which leads to significant performance degradation on different datasets. In this paper, we introduce a novel adversarial attack approach under the decision-based black-box setting, where the attack does not have access to the model parameters, and the returned information from querying the target model consists of only the final class label prediction (i.e., hard-label attack). The proposed attack approach has two major components: adaptive binary search and semantic-aware search. The adaptive binary search utilizes a coarse-to-fine strategy that applies adaptive tolerance values in different searching stages to reduce unnecessary queries. The proposed semantic mask-aware search crops the search space by using breast anatomy, which significantly avoids invalid searches. We validate the proposed approach using a dataset of 3378 breast ultrasound images and compare it with another state-of-the-art method by attacking five deep learning models. The results demonstrate that the proposed approach generates imperceptible adversarial samples at a high success rate (between 99.52% and 100%), and dramatically reduces the average and median queries by 23.96% and 31.79%, respectively, compared with the state-of-the-art approach.