BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection

• URL: http://arxiv.org/abs/2405.03884v1
• Date: Mon, 6 May 2024 22:02:38 GMT
• Title: BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection
• Authors: Saket S. Chaturvedi, Lan Zhang, Wenbin Zhang, Pan He, Xiaoyong Yuan,
• Abstract summary: 3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. Existing backdoor attacks against 3D object detection primarily poison 3D LiDAR signals. We propose an innovative 2D-oriented backdoor attack against LiDAR-camera fusion methods for 3D object detection, named BadFusion.
• Score: 12.987427748635037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. By injecting ''triggers'' to poison the training dataset, backdoor attacks manipulate the detector's prediction for inputs containing these triggers. Existing backdoor attacks against 3D object detection primarily poison 3D LiDAR signals, where large-sized 3D triggers are injected to ensure their visibility within the sparse 3D space, rendering them easy to detect and impractical in real-world scenarios. In this paper, we delve into the robustness of 3D object detection, exploring a new backdoor attack surface through 2D cameras. Given the prevalent adoption of camera and LiDAR signal fusion for high-fidelity 3D perception, we investigate the latent potential of camera signals to disrupt the process. Although the dense nature of camera signals enables the use of nearly imperceptible small-sized triggers to mislead 2D object detection, realizing 2D-oriented backdoor attacks against 3D object detection is non-trivial. The primary challenge emerges from the fusion process that transforms camera signals into a 3D space, compromising the association with the 2D trigger to the target output. To tackle this issue, we propose an innovative 2D-oriented backdoor attack against LiDAR-camera fusion methods for 3D object detection, named BadFusion, for preserving trigger effectiveness throughout the entire fusion process. The evaluation demonstrates the effectiveness of BadFusion, achieving a significantly higher attack success rate compared to existing 2D-oriented attacks.

Related papers

• AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training [64.14759275211115]
  We propose a depth-aware robust adversarial training method for monocular 3D object detection, dubbed DART3D. Our adversarial training approach capitalizes on the inherent uncertainty, enabling the model to significantly improve its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2023-09-03T07:05:32Z)
• FocalFormer3D : Focusing on Hard Instance for 3D Object Detection [97.56185033488168]
  False negatives (FN) in 3D object detection can lead to potentially dangerous situations in autonomous driving. In this work, we propose Hard Instance Probing (HIP), a general pipeline that identifies textitFN in a multi-stage manner. We instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects.
  arXiv  Detail & Related papers  (2023-08-08T20:06:12Z)
• A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks [84.10546708708554]
  3D object detectors are increasingly crucial for security-critical tasks. It is imperative to understand their robustness against adversarial attacks. This paper presents the first comprehensive evaluation and analysis of the robustness of LiDAR-based 3D detectors under adversarial attacks.
  arXiv  Detail & Related papers  (2022-12-20T13:09:58Z)
• SparseDet: Towards End-to-End 3D Object Detection [12.3069609175534]
  We propose SparseDet for end-to-end 3D object detection from point cloud. As a new detection paradigm, SparseDet maintains a fixed set of learnable proposals to represent latent candidates. SparseDet achieves highly competitive detection accuracy while running with a more efficient speed of 34.5 FPS.
  arXiv  Detail & Related papers  (2022-06-02T09:49:53Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud [79.39041453836793]
  We develop a novel single-stage 3D detector for point clouds in an anchor-free manner. We overcome this by converting the voxel-based sparse 3D feature volumes into the sparse 2D feature maps. We propose an IoU-based detection confidence re-calibration scheme to improve the correlation between the detection confidence score and the accuracy of the bounding box regression.
  arXiv  Detail & Related papers  (2021-08-08T13:42:13Z)
• FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [78.00922683083776]
  It is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020.
  arXiv  Detail & Related papers  (2021-04-22T09:35:35Z)
• RoIFusion: 3D Object Detection from LiDAR and Vision [7.878027048763662]
  We propose a novel fusion algorithm by projecting a set of 3D Region of Interests (RoIs) from the point clouds to the 2D RoIs of the corresponding the images. Our approach achieves state-of-the-art performance on the KITTI 3D object detection challenging benchmark.
  arXiv  Detail & Related papers  (2020-09-09T20:23:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.