  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 of their implications for object detection. Object
detection plays a critical role in security-sensitive applications, such as
autonomous driving, where backdoor attacks seriously threaten human life and
property. In this work, we propose the first backdoor attack designed for
object detection tasks in SSL scenarios, called 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: Naive
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 of our proposed attack and its resistance to potential
defenses. 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.
