Learning to Adversarially Blur Visual Object Tracking-猫眼课题宝

DOI：

10.1109/iccv48922.2021.01066

发表时间：

2021-07

期刊：

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Qing Guo;Ziyi Cheng;Felix Juefei-Xu;L. Ma;Xiaofei Xie;Yang Liu;Jianjun Zhao

在曝光期间由物体或相机的移动引起的运动模糊可以是视觉对象跟踪的关键挑战，显著影响跟踪精度。在这项工作中，我们从一个新的角度，即探索视觉对象跟踪器对运动模糊的鲁棒性，对抗性模糊攻击（阿坝）。我们的主要目标是将输入帧在线传输到其自然运动模糊的对应帧，同时在跟踪过程中误导最先进的跟踪器。为此，我们首先根据运动模糊的产生原理，考虑到运动信息和光积累过程，设计了用于视觉跟踪的运动模糊合成方法。通过这种合成方法，我们提出了基于优化的阿坝（OP-ABA），通过迭代优化对抗跟踪w.r.t.运动和光累积参数。OP-ABA能够产生自然的对抗性示例，但迭代会导致大量的时间成本，使其不适合攻击实时跟踪器。为了缓解这个问题，我们进一步提出了一步阿坝（OS-ABA），在OP-ABA的指导下，我们设计和训练了一个联合对抗运动和积累预测网络（JAMANet），它能够以一步的方式有效地估计对抗运动和积累参数。在四个流行的数据集上的实验（例如，OTB 100，VOT 2018，UAV 123和LaSOT）证明，我们的方法能够在四个具有高可转移性的最先进的跟踪器上导致显著的准确性下降。请在https://github.com/tsingqguo/ABA上找到源代码

Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur attack (ABA). Our main objective is to online transfer input frames to their natural motion-blurred counterparts while misleading the state-of-the-art trackers during the tracking process. To this end, we first design the motion blur synthesizing method for visual tracking based on the generation principle of motion blur, considering the motion information and the light accumulation process. With this synthetic method, we propose optimization-based ABA (OP-ABA) by iteratively optimizing an adversarial objective function against the tracking w.r.t. the motion and light accumulation parameters. The OP-ABA is able to produce natural adversarial examples but the iteration can cause heavy time cost, making it unsuitable for attacking real-time trackers. To alleviate this issue, we further propose one-step ABA (OS-ABA) where we design and train a joint adversarial motion and accumulation predictive network (JAMANet) with the guidance of OP-ABA, which is able to efficiently estimate the adversarial motion and accumulation parameters in a one-step way. The experiments on four popular datasets (e.g., OTB100, VOT2018, UAV123, and LaSOT) demonstrate that our methods are able to cause significant accuracy drops on four state-of-the-art trackers with high transferability. Please find the source code at https://github.com/tsingqguo/ABA

Learning to Adversarially Blur Visual Object Tracking

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