SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for
  Object Detection
Qiannan Wang Changchun Yin Liming Fang Lu Zhou Zhe Liu Run Wang Chenhao Lin
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.