ADVERSARY DISTILLATION FOR ONE-SHOT ATTACKS ON 3D TARGET TRACKING

Zhengyi Wang, Xupeng Wang, Ferdous Sohel, Mohammed Bennamoun, Yong Liao, Jiali Yu

Research output: Chapter in Book/Conference paperConference paperpeer-review

2 Citations (Scopus)

Abstract

Considering the vulnerability of existing deep models in the adversarial scenario, the robustness of 3D target tracking is not guaranteed. In this paper, we present an efficient generation based adversarial attack, termed Adversary Distillation Network (AD-Net), which is able to distract a victim tracker in a single shot. In contrast to existing adversarial attacks derived from point perturbations, the proposed method designs a generative network to distill an adversarial example from a tracking template through point-wise filtration. A binary distribution encoding layer is specialized to filter points, which is modeled as a Bernoulli distribution and approximated in a differentiable formulation. To boost the performance of adversarial example generation, a feature extraction module is deployed, which leverages the PointNet++ architecture to learn hierarchical features for the template points as well as similarities with the search areas. Experimental results on the KITTI vision benchmark show that the proposed adversarial attack can effectively mislead popular deep 3D trackers.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2749-2753
Number of pages5
ISBN (Electronic)9781665405409
ISBN (Print)978-1-6654-0541-6
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23/05/2227/05/22

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