LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks

• URL: http://arxiv.org/abs/2404.06247v1
• Date: Tue, 9 Apr 2024 12:13:40 GMT
• Title: LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
• Abstract summary: State-of-the-art (SOTA) trackers often fail when faced with adversarial perturbations in the incoming frames. We propose building a spatial-temporal continuous representation using the semantic text guidance of the object of interest. Our method significantly increases tracking accuracy under adversarial attacks with around 90% relative improvement on UAV123.
• Score: 27.251934234589967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual object tracking plays a critical role in visual-based autonomous systems, as it aims to estimate the position and size of the object of interest within a live video. Despite significant progress made in this field, state-of-the-art (SOTA) trackers often fail when faced with adversarial perturbations in the incoming frames. This can lead to significant robustness and security issues when these trackers are deployed in the real world. To achieve high accuracy on both clean and adversarial data, we propose building a spatial-temporal continuous representation using the semantic text guidance of the object of interest. This novel continuous representation enables us to reconstruct incoming frames to maintain semantic and appearance consistency with the object of interest and its clean counterparts. As a result, our proposed method successfully defends against different SOTA adversarial tracking attacks while maintaining high accuracy on clean data. In particular, our method significantly increases tracking accuracy under adversarial attacks with around 90% relative improvement on UAV123, which is even higher than the accuracy on clean data.

Related papers

• Adversarial Attacks Already Tell the Answer: Directional Bias-Guided Test-time Defense for Vision-Language Models [44.54124778801062]
  Test-time defenses for vision-Language Models (VLMs) have emerged as a promising and efficient approach to defend against adversarial attacks without requiring large-scale retraining.<n>We propose Directional Bias-guided Defense (DBD), a test-time framework that estimates the Defense Direction and employs a DB-score-based two-stream reconstruction strategy to recover robust representations.<n>Experiments on 15 datasets demonstrate that DBD not only achieves SOTA adversarial robustness while preserving clean accuracy, but also reveals the counterintuitive result that adversarial accuracy can even surpass clean accuracy.
  arXiv  Detail & Related papers  (2026-06-04T13:57:07Z)
• Delving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking [16.366398265001422]
  3D multi-object tracking is a critical and challenging task in the field of autonomous driving.<n>We introduce the Dynamic Scene Cue-Consistency Tracker (DSC-Track) to implement this principle.
  arXiv  Detail & Related papers  (2025-08-15T08:48:13Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)
• Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking [2.487142846438629]
  3 single object tracking within LIDAR point is pivotal task in computer vision. Existing methods, which depend solely on appearance matching via networks or utilize information from successive frames, encounter significant challenges. We design an innovative cross-frame bi-temporal motion tracker, named STMD-Tracker, to mitigate these challenges.
  arXiv  Detail & Related papers  (2024-03-23T13:15:44Z)
• Context-aware Visual Tracking with Joint Meta-updating [11.226947525556813]
  We propose a context-aware tracking model to optimize the tracker over the representation space, which jointly meta-update both branches by exploiting information along the whole sequence. The proposed tracking method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS, demonstrating its capability of improving the accuracy and robustness of the underlying tracker with little speed drop.
  arXiv  Detail & Related papers  (2022-04-04T14:16:00Z)
• Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking [150.51667609413312]
  This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN) for visual object tracking. To address this problem, CD-CNN models temporal appearance continuity based on the idea of temporal slowness. In order to alleviate inaccurate target localization and drifting, we propose a novel notion, object-centroid.
  arXiv  Detail & Related papers  (2021-04-18T06:35:03Z)
• 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)
• 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)
• Spatiotemporal Attacks for Embodied Agents [119.43832001301041]
  We take the first step to study adversarial attacks for embodied agents. In particular, we generate adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions. Our perturbations have strong attack and generalization abilities.
  arXiv  Detail & Related papers  (2020-05-19T01:38:47Z)

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.