论文标题
暂时转移的扰动：在线视觉对象跟踪器的高效，单发对手攻击
Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers
论文作者
论文摘要
近年来，基于暹罗网络的跟踪器已成为视觉对象跟踪（fot）的高效和高效。尽管这些方法被证明容易受到对抗攻击的影响，但作为大多数用于视觉识别任务的深层网络，投票跟踪器的现有攻击都需要使每个输入框架的搜索区域都有效，这是有效的，这是不可忽略的成本，考虑到fot是一项实时任务。在本文中，我们提出了一个框架，以仅从对象模板图像中生成单个可转移的对抗扰动。然后可以将这种扰动添加到每个搜索映像中，几乎无需花费，但仍然成功地欺骗了跟踪器。我们的实验证明我们的方法在未定位的情况下超过了对标准投票基准的最先进攻击。此外，我们表明我们的形式主义自然扩展到有针对性的攻击，这些攻击迫使跟踪器通过对各种方向扰动进行预扰来遵循任何给定的轨迹。
In recent years, the trackers based on Siamese networks have emerged as highly effective and efficient for visual object tracking (VOT). While these methods were shown to be vulnerable to adversarial attacks, as most deep networks for visual recognition tasks, the existing attacks for VOT trackers all require perturbing the search region of every input frame to be effective, which comes at a non-negligible cost, considering that VOT is a real-time task. In this paper, 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. Our experiments evidence that our approach outperforms the state-of-the-art attacks on the standard VOT benchmarks in the untargeted scenario. Furthermore, we show that our formalism naturally extends to targeted attacks that force the tracker to follow any given trajectory by precomputing diverse directional perturbations.
