# General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments Source: [General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:52:04.916Z Authors: Xiaogang Xu, Hengshuang Zhao, Philip Torr, Jiaya Jia Published: 2022-12-11T01:51:31.000Z ## Abstract Deep Neural Networks (DNNs) are vulnerable to the black-box adversarial attack that is highly transferable. This threat comes from the distribution gap between adversarial and clean samples in feature space of the target DNNs. In this paper, we use Deep Generative Networks (DGNs) with a novel training mechanism to eliminate the distribution gap. The trained DGNs align the distribution of adversarial samples with clean ones for the target DNNs by translating pixel values. Different from previous work, we propose a more effective pixel level training constraint to make this achievable, thus enhancing robustness on adversarial samples. Further, a class-aware feature-level constraint is formulated for integrated distribution alignment. Our approach is general and applicable to multiple tasks, including image classification, semantic segmentation, and object detection. We conduct extensive experiments on different datasets. Our strategy demonstrates its unique effectiveness and generality against black-box attacks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()