Learning to Adversarially Blur Visual Object Tracking

TL;DR

This paper introduces adversarial blur attacks that generate natural-looking motion-blurred frames to deceive state-of-the-art visual object trackers, revealing vulnerabilities and improving understanding of tracker robustness.

Contribution

It proposes a novel synthetic motion blur method and two adversarial attack techniques, OP-ABA and OS-ABA, to effectively fool visual object trackers with high transferability.

Findings

Adversarial blur attacks significantly reduce tracker accuracy.

The proposed methods outperform baseline attacks in effectiveness.

High transferability of attacks across different datasets and trackers.

Abstract

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 \url{https://github.com/tsingqguo/ABA}.