SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking

Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, Yang Liu

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Adversarial attacks of deep neural networks have been intensively studied on image, audio, and natural language classification tasks. Nevertheless, as a typical while important real-world application, the adversarial attacks of online video tracking that traces an object’s moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along with an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, i.e., FGSM, BIM, and C&W, and comprehensively analyze the attacking performance. We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency. To address these challenges, we further propose the spatial-aware online inc remental attac k (a.k.a. SPARK) that performs spatial-temporal sparse incremental perturbations online and makes the adversarial attack less perceptible. In addition, as an optimization-based method, SPARK quickly converges to very small losses within several iterations by considering historical incremental perturbations, making it much more efficient than basic attacks. The in-depth evaluation of the state-of-the-art trackers (i.e., SiamRPN++ with AlexNet, MobileNetv2, and ResNet-50, and SiamDW) on OTB100, VOT2018, UAV123, and LaSOT demonstrates the effectiveness and transferability of SPARK in misleading the trackers under both UA and TA with minor perturbations.

本文言語英語
ホスト出版物のタイトルComputer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
編集者Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
出版社Springer Science and Business Media Deutschland GmbH
ページ202-219
ページ数18
ISBN(印刷版)9783030585945
DOI
出版ステータス出版済み - 2020
イベント16th European Conference on Computer Vision, ECCV 2020 - Glasgow, 英国
継続期間: 8 23 20208 28 2020

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12370 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議16th European Conference on Computer Vision, ECCV 2020
Country英国
CityGlasgow
Period8/23/208/28/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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