Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers

• URL: http://arxiv.org/abs/2406.01765v1
• Date: Mon, 3 Jun 2024 20:13:38 GMT
• Title: Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
• Authors: Fatemeh Nourilenjan Nokabadi, Jean-François Lalonde, Christian Gagné,
• Abstract summary: New transformer networks have been integrated into object tracking pipelines and have demonstrated strong performance on the latest benchmarks. This paper focuses on understanding how transformer trackers behave under adversarial attacks and how different attacks perform on tracking datasets as their parameters change.
• Score: 18.615714086028632
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: New transformer networks have been integrated into object tracking pipelines and have demonstrated strong performance on the latest benchmarks. This paper focuses on understanding how transformer trackers behave under adversarial attacks and how different attacks perform on tracking datasets as their parameters change. We conducted a series of experiments to evaluate the effectiveness of existing adversarial attacks on object trackers with transformer and non-transformer backbones. We experimented on 7 different trackers, including 3 that are transformer-based, and 4 which leverage other architectures. These trackers are tested against 4 recent attack methods to assess their performance and robustness on VOT2022ST, UAV123 and GOT10k datasets. Our empirical study focuses on evaluating adversarial robustness of object trackers based on bounding box versus binary mask predictions, and attack methods at different levels of perturbations. Interestingly, our study found that altering the perturbation level may not significantly affect the overall object tracking results after the attack. Similarly, the sparsity and imperceptibility of the attack perturbations may remain stable against perturbation level shifts. By applying a specific attack on all transformer trackers, we show that new transformer trackers having a stronger cross-attention modeling achieve a greater adversarial robustness on tracking datasets, such as VOT2022ST and GOT10k. Our results also indicate the necessity for new attack methods to effectively tackle the latest types of transformer trackers. The codes necessary to reproduce this study are available at https://github.com/fatemehN/ReproducibilityStudy.

Related papers

• Adversarial Robustness of In-Context Learning in Transformers for Linear Regression [23.737606860443705]
  This work investigates the vulnerability of in-context learning in transformers to textithijacking attacks focusing on the setting of linear regression tasks. We first prove that single-layer linear transformers, known to implement gradient descent in-context, are non-robust and can be manipulated to output arbitrary predictions. We then demonstrate that adversarial training enhances transformers' robustness against hijacking attacks, even when just applied during finetuning.
  arXiv  Detail & Related papers  (2024-11-07T21:25:58Z)
• Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers [95.22517830759193]
  This paper studies the transferability of such an adversarial vulnerability from a pre-trained ViT model to downstream tasks. We show that DTA achieves an average attack success rate (ASR) exceeding 90%, surpassing existing methods by a huge margin.
  arXiv  Detail & Related papers  (2024-08-03T08:07:03Z)
• TrackPGD: A White-box Attack using Binary Masks against Robust Transformer Trackers [6.115755665318123]
  Object trackers with transformer backbones have achieved robust performance on visual object tracking datasets. Due to the backbone differences, the adversarial white-box attacks proposed for object tracking are not transferable to all types of trackers. We are proposing a novel white-box attack named TrackPGD, which relies on the predicted object binary mask to attack the robust transformer trackers.
  arXiv  Detail & Related papers  (2024-07-04T14:02:12Z)
• Adaptively Bypassing Vision Transformer Blocks for Efficient Visual Tracking [11.361394596302334]
  ABTrack is an adaptive computation framework that adaptively bypassing transformer blocks for efficient visual tracking. We propose a Bypass Decision Module (BDM) to determine if a transformer block should be bypassed. We introduce a novel ViT pruning method to reduce the dimension of the latent representation of tokens in each transformer block.
  arXiv  Detail & Related papers  (2024-06-12T09:39:18Z)
• The Efficacy of Transformer-based Adversarial Attacks in Security Domains [0.7156877824959499]
  We evaluate the robustness of transformers to adversarial samples for system defenders and their adversarial strength for system attackers. Our work emphasizes the importance of studying transformer architectures for attacking and defending models in security domains.
  arXiv  Detail & Related papers  (2023-10-17T21:45:23Z)
• Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
  We propose two data augmentation methods customized for tracking. First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples. Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
  arXiv  Detail & Related papers  (2023-09-15T09:18:54Z)
• Emergent Agentic Transformer from Chain of Hindsight Experience [96.56164427726203]
  We show that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches. This is the first time that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches.
  arXiv  Detail & Related papers  (2023-05-26T00:43:02Z)
• Efficient Visual Tracking with Exemplar Transformers [98.62550635320514]
  We introduce the Exemplar Transformer, an efficient transformer for real-time visual object tracking. E.T.Track, our visual tracker that incorporates Exemplar Transformer layers, runs at 47 fps on a CPU. This is up to 8 times faster than other transformer-based models.
  arXiv  Detail & Related papers  (2021-12-17T18:57:54Z)
• DBIA: Data-free Backdoor Injection Attack against Transformer Networks [6.969019759456717]
  We propose DBIA, a data-free backdoor attack against the CV-oriented transformer networks. Our approach can embed backdoors with a high success rate and a low impact on the performance of the victim transformers.
  arXiv  Detail & Related papers  (2021-11-22T08:13:51Z)
• Towards Transferable Adversarial Attacks on Vision Transformers [110.55845478440807]
  Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. We introduce a dual attack framework, which contains a Pay No Attention (PNA) attack and a PatchOut attack, to improve the transferability of adversarial samples across different ViTs.
  arXiv  Detail & Related papers  (2021-09-09T11:28:25Z)
• On the Adversarial Robustness of Visual Transformers [129.29523847765952]
  This work provides the first and comprehensive study on the robustness of vision transformers (ViTs) against adversarial perturbations. Tested on various white-box and transfer attack settings, we find that ViTs possess better adversarial robustness when compared with convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2021-03-29T14:48:24Z)

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.