Papers › BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks

BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks

1 Apr 2024arXiv:2404.00924archive 2025-07-28

Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo, Shiwei Feng, Dongfang Liu, Mingjie Tang, Xiangyu Zhang

Pixel-wise regression tasks (e.g., monocular depth estimation (MDE) and optical flow estimation (OFE)) have been widely involved in our daily life in applications like autonomous driving, augmented reality and video composition. Although certain applications are security-critical or bear societal significance, the adversarial robustness of such models are not sufficiently studied, especially in the black-box scenario. In this work, we introduce the first unified black-box adversarial patch attack framework against pixel-wise regression tasks, aiming to identify the vulnerabilities of these models under query-based black-box attacks. We propose a novel square-based adversarial patch optimization framework and employ probabilistic square sampling and score-based gradient estimation techniques to generate the patch effectively and efficiently, overcoming the scalability problem of previous black-box patch attacks. Our attack prototype, named BadPart, is evaluated on both MDE and OFE tasks, utilizing a total of 7 models. BadPart surpasses 3 baseline methods in terms of both attack performance and efficiency. We also apply BadPart on the Google online service for portrait depth estimation, causing 43.5% relative distance error with 50K queries. State-of-the-art (SOTA) countermeasures cannot defend our attack effectively.

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compute_cossim Bob-cheng/BadPart/FlowNetworks/flow_models/losses.py official repository ran no licence file found · pointer only · cabc2ac9ef60e73b · report
compute_epe Bob-cheng/BadPart/FlowNetworks/flow_models/losses.py official repository ran fingerprinted no licence file found · pointer only · 795e0718dd9ab1ca · report
depth_to_disp Bob-cheng/BadPart/attack/depth_model.py official repository ran no licence file found · pointer only · 327b4158b7b40685 · report
disp_to_depth Bob-cheng/BadPart/attack/depth_model.py official repository ran no licence file found · pointer only · 62287188376f0ba0 · report
disp_to_depth bob-cheng/badpart/my_utils.py official repository ran no licence file found · pointer only · 5ddbb1ceaa5ee4e9 · report
load_and_preprocess_intrinsics Bob-cheng/BadPart/attack/depth_model.py official repository ran no licence file found · pointer only · 4e589c19c316dbe1 · report
multiscale_cossim Bob-cheng/BadPart/FlowNetworks/flow_models/losses.py official repository ran no licence file found · pointer only · 245771e9a240453a · report
softmax_parent_selection Bob-cheng/BadPart/my_utils.py official repository ran no licence file found · pointer only · e012e267c56a070d · report
import_optical_flow_model Bob-cheng/BadPart/attack/flow_model.py official repository unverified no licence file found · pointer only · 14f4a864ffa4c082 · report

Tasks

Adversarial RobustnessAutonomous DrivingDepth EstimationMonocular Depth EstimationOptical Flow Estimationregression

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