Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks

• URL: http://arxiv.org/abs/2402.17976v2
• Date: Fri, 12 Jul 2024 18:36:50 GMT
• Title: Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks
• Authors: Zhewei Wu, Ruilong Yu, Qihe Liu, Shuying Cheng, Shilin Qiu, Shijie Zhou,
• Abstract summary: Adrial attacks in visual object tracking have significantly degraded the performance of advanced trackers. We propose an effective auxiliary pre-processing defense network, AADN, which performs defensive transformations on the input images before feeding them into the tracker.
• Score: 1.7907721703063868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial attacks in visual object tracking have significantly degraded the performance of advanced trackers by introducing imperceptible perturbations into images. However, there is still a lack of research on designing adversarial defense methods for object tracking. To address these issues, we propose an effective auxiliary pre-processing defense network, AADN, which performs defensive transformations on the input images before feeding them into the tracker. Moreover, it can be seamlessly integrated with other visual trackers as a plug-and-play module without parameter adjustments. We train AADN using adversarial training, specifically employing Dua-Loss to generate adversarial samples that simultaneously attack the classification and regression branches of the tracker. Extensive experiments conducted on the OTB100, LaSOT, and VOT2018 benchmarks demonstrate that AADN maintains excellent defense robustness against adversarial attack methods in both adaptive and non-adaptive attack scenarios. Moreover, when transferring the defense network to heterogeneous trackers, it exhibits reliable transferability. Finally, AADN achieves a processing time of up to 5ms/frame, allowing seamless integration with existing high-speed trackers without introducing significant computational overhead.

Related papers

• DAD++: Improved Data-free Test Time Adversarial Defense [12.606555446261668]
  We propose a test time Data-free Adversarial Defense (DAD) containing detection and correction frameworks. We conduct a wide range of experiments and ablations on several datasets and network architectures to show the efficacy of our proposed approach. Our DAD++ gives an impressive performance against various adversarial attacks with a minimal drop in clean accuracy.
  arXiv  Detail & Related papers  (2023-09-10T20:39:53Z)
• Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking [15.38386172273694]
  This work proposes a novel adaptive adversarial attack approach, i.e., Ad$2$Attack, against UAV object tracking. A novel optimization function is proposed for balancing the imperceptibility and efficiency of the attack. Experiments on several well-known benchmarks and real-world conditions show the effectiveness of our attack method.
  arXiv  Detail & Related papers  (2022-03-03T05:00:32Z)
• Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning [95.60856995067083]
  This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms. We propose to perform adversarial defense from two perspectives: 1) adversarial perturbation purification and 2) adversarial perturbation detection. Experimental results show that our detection module effectively shields the ASV by detecting adversarial samples with an accuracy of around 80%.
  arXiv  Detail & Related papers  (2021-06-01T07:10:54Z)
• Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers [81.90113217334424]
  We propose a framework to generate a single temporally transferable adversarial perturbation from the object template image only. This perturbation can then be added to every search image, which comes at virtually no cost, and still, successfully fool the tracker.
  arXiv  Detail & Related papers  (2020-12-30T15:05:53Z)
• Efficient Adversarial Attacks for Visual Object Tracking [73.43180372379594]
  We present an end-to-end network FAN (Fast Attack Network) that uses a novel drift loss combined with the embedded feature loss to attack the Siamese network based trackers. Under a single GPU, FAN is efficient in the training speed and has a strong attack performance.
  arXiv  Detail & Related papers  (2020-08-01T08:47:58Z)
• Robust Tracking against Adversarial Attacks [69.59717023941126]
  We first attempt to generate adversarial examples on top of video sequences to improve the tracking robustness against adversarial attacks. We apply the proposed adversarial attack and defense approaches to state-of-the-art deep tracking algorithms.
  arXiv  Detail & Related papers  (2020-07-20T08:05:55Z)
• Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification [78.51092318750102]
  This work proposes to defend ASV systems against adversarial attacks with a separate detection network. A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples.
  arXiv  Detail & Related papers  (2020-06-11T04:31:56Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

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.