标题： 具有扩散模型的鲁棒视觉跟踪的有效且高效的对抗防御研究 显示英文标题

标题： Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual Tracking

摘要： 尽管基于深度学习的视觉跟踪方法取得了显著进展，但在面对精心设计的对抗性攻击时表现出脆弱性，这可能导致跟踪性能急剧下降。 为了解决这一问题，本文首次提出了一种新颖的基于去噪扩散概率模型的对抗防御方法，称为DiffDf，旨在有效提高现有视觉跟踪方法对对抗性攻击的鲁棒性。 DiffDf通过结合像素级重建损失、语义一致性损失和结构相似性损失，建立了多尺度防御机制，在渐进去噪过程中有效地抑制了对抗扰动。 在多个主流数据集上的广泛实验结果表明，DiffDf方法对具有不同架构的跟踪器表现出优异的泛化性能，显著提升了各种评估指标，同时实现了超过30 FPS的实时推理速度，展示了出色的防御性能和效率。 代码可在https://github.com/pgao-lab/DiffDf获取。 显示英文摘要

摘要： Although deep learning-based visual tracking methods have made significant progress, they exhibit vulnerabilities when facing carefully designed adversarial attacks, which can lead to a sharp decline in tracking performance. To address this issue, this paper proposes for the first time a novel adversarial defense method based on denoise diffusion probabilistic models, termed DiffDf, aimed at effectively improving the robustness of existing visual tracking methods against adversarial attacks. DiffDf establishes a multi-scale defense mechanism by combining pixel-level reconstruction loss, semantic consistency loss, and structural similarity loss, effectively suppressing adversarial perturbations through a gradual denoising process. Extensive experimental results on several mainstream datasets show that the DiffDf method demonstrates excellent generalization performance for trackers with different architectures, significantly improving various evaluation metrics while achieving real-time inference speeds of over 30 FPS, showcasing outstanding defense performance and efficiency. Codes are available at https://github.com/pgao-lab/DiffDf.