  Intent obfuscation is a common tactic in adversarial situations, enabling the
attacker to both manipulate the target system and avoid culpability.
Surprisingly, it has rarely been implemented in adversarial attacks on machine
learning systems. We are the first to propose incorporating intent obfuscation
in generating adversarial examples for object detectors: by perturbing another
non-overlapping object to disrupt the target object, the attacker hides their
intended target. We conduct a randomized experiment on 5 prominent detectors --
YOLOv3, SSD, RetinaNet, Faster R-CNN, and Cascade R-CNN -- using both targeted
and untargeted attacks and achieve success on all models and attacks. We
analyze the success factors characterizing intent obfuscating attacks,
including target object confidence and perturb object sizes. We then
demonstrate that the attacker can exploit these success factors to increase
success rates for all models and attacks. Finally, we discuss known defenses
and legal repercussions.
