BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2509.19793v1
• Date: Wed, 24 Sep 2025 06:27:15 GMT
• Title: BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting
• Authors: Yixun Zhang, Feng Zhou, Jianqin Yin,
• Abstract summary: We present BiTAA, a bi-task adversarial attack built on 3D Gaussian Splatting.<n>Specifically, we introduce a dual-model attack framework that supports both full-image and patch settings.<n>We also propose a unified evaluation protocol with cross-task transfer metrics and real-world evaluations.
• Score: 14.918777539580978
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Camera-based perception is critical to autonomous driving yet remains vulnerable to task-specific adversarial manipulations in object detection and monocular depth estimation. Most existing 2D/3D attacks are developed in task silos, lack mechanisms to induce controllable depth bias, and offer no standardized protocol to quantify cross-task transfer, leaving the interaction between detection and depth underexplored. We present BiTAA, a bi-task adversarial attack built on 3D Gaussian Splatting that yields a single perturbation capable of simultaneously degrading detection and biasing monocular depth. Specifically, we introduce a dual-model attack framework that supports both full-image and patch settings and is compatible with common detectors and depth estimators, with optional expectation-over-transformation (EOT) for physical reality. In addition, we design a composite loss that couples detection suppression with a signed, magnitude-controlled log-depth bias within regions of interest (ROIs) enabling controllable near or far misperception while maintaining stable optimization across tasks. We also propose a unified evaluation protocol with cross-task transfer metrics and real-world evaluations, showing consistent cross-task degradation and a clear asymmetry between Det to Depth and from Depth to Det transfer. The results highlight practical risks for multi-task camera-only perception and motivate cross-task-aware defenses in autonomous driving scenarios.

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