针对视觉追踪中的多模态数据，提出跨模态对抗攻击方法。
Adversarial Attack for RGB-Event based Visual Object Tracking
- 通过优化扰动生成跨模态对抗样本，提升攻击效果。
- 在三个主流数据集上显著降低追踪器性能，验证方法有效性。
- 适合研究对抗攻防与多模态视觉系统的学者参考。
视觉目标追踪是计算机视觉与多模态融合领域的关键课题。结合RGB帧与事件流的鲁棒追踪方法受到越来越多关注。然而，针对此类算法的对抗攻击与防御研究仍较匮乏。为此，本文提出一种面向RGB-Event视觉追踪的跨模态对抗攻击算法。由于事件流表示方式多样，本文聚焦于事件体素（Event voxels）与事件帧（Event frames）两种常见形式。对于RGB-Event体素，先通过对抗损失优化扰动，生成RGB帧的对抗样本；对于离散事件体素，采用两步攻击策略：首先将事件体素注入目标区域作为初始对抗样本，再通过梯度引导优化其空间位置。对于基于事件帧的追踪，通过融合多模态梯度信息，优化跨模态通用扰动。在COESOT、FE108和VisEvent三个主流数据集上进行评估，实验表明，该方法在单模态与多模态场景下均显著降低追踪器性能。源代码将公开于https://github.com/Event-AHU/Adversarial_Attack_Defense。
原文摘要 · Abstract (English)
Visual object tracking is a crucial research topic in the fields of computer vision and multi-modal fusion. Among various approaches, robust visual tracking that combines RGB frames with Event streams has attracted increasing attention from researchers. While striving for high accuracy and efficiency in tracking, it is also important to explore how to effectively conduct adversarial attacks and defenses on RGB-Event stream tracking algorithms, yet research in this area remains relatively scarce. To bridge this gap, in this paper, we propose a cross-modal adversarial attack algorithm for RGB-Event visual tracking. Because of the diverse representations of Event streams, and given that Event voxels and frames are more commonly used, this paper will focus on these two representations for an in-depth study. Specifically, for the RGB-Event voxel, we first optimize the perturbation by adversarial loss to generate RGB frame adversarial examples. For discrete Event voxel representations, we propose a two-step attack strategy, more in detail, we first inject Event voxels into the target region as initialized adversarial examples, then, conduct a gradient-guided optimization by perturbing the spatial location of the Event voxels. For the RGB-Event frame based tracking, we optimize the cross-modal universal perturbation by integrating the gradient information from multimodal data. We evaluate the proposed approach against attacks on three widely used RGB-Event Tracking datasets, i.e., COESOT, FE108, and VisEvent. Extensive experiments show that our method significantly reduces the performance of the tracker across numerous datasets in both unimodal and multimodal scenarios. The source code will be released on https://github.com/Event-AHU/Adversarial_Attack_Defense
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