The extensive adoption of Self-supervised learning (SSL) has led to an increased security threat from backdoor attacks. While existing research has mainly focused on backdoor attacks in image classification, there has been limited exploration into their implications for object detection. In this work, we propose the first backdoor attack designed for object detection tasks in SSL scenarios, termed Object Transform Attack (SSL-OTA). SSL-OTA employs a trigger capable of altering predictions of the target object to the desired category, encompassing two attacks: Data Poisoning Attack (NA) and Dual-Source Blending Attack (DSBA). NA conducts data poisoning during downstream fine-tuning of the object detector, while DSBA additionally injects backdoors into the pre-trained encoder. We establish appropriate metrics and conduct extensive experiments on benchmark datasets, demonstrating the effectiveness and utility of our proposed attack. Notably, both NA and DSBA achieve high attack success rates (ASR) at extremely low poisoning rates (0.5%). The results underscore the importance of considering backdoor threats in SSL-based object detection and contribute a novel perspective to the field.

我们提出了一种用于自我监督学习场景下对象检测任务的首个后门攻击方法，名为对象变换攻击（SSL-OTA）。该攻击方法包括两种攻击：数据毒化攻击（NA）和双源混合攻击（DSBA），并且在基准数据集上进行了广泛实验证明了攻击的有效性和实用性，这突出了在基于自我监督学习的对象检测中考虑后门威胁的重要性，并为该领域提供了一种新的视角。

SSL-OTA：自我监督学习中目标检测的后门威胁揭示