Towards Generic and Controllable Attacks Against Object Detection
Guopeng Li Yue Xu Jian Ding Gui-Song Xia
Existing adversarial attacks against Object Detectors (ODs) suffer from two
inherent limitations. Firstly, ODs have complicated meta-structure designs,
hence most advanced attacks for ODs concentrate on attacking specific
detector-intrinsic structures, which makes it hard for them to work on other
detectors and motivates us to design a generic attack against ODs. Secondly,
most works against ODs make Adversarial Examples (AEs) by generalizing
image-level attacks from classification to detection, which brings redundant
computations and perturbations in semantically meaningless areas (e.g.,
backgrounds) and leads to an emergency for seeking controllable attacks for
ODs. To this end, we propose a generic white-box attack, LGP (local
perturbations with adaptively global attacks), to blind mainstream object
detectors with controllable perturbations. For a detector-agnostic attack, LGP
tracks high-quality proposals and optimizes three heterogeneous losses
simultaneously. In this way, we can fool the crucial components of ODs with a
part of their outputs without the limitations of specific structures. Regarding
controllability, we establish an object-wise constraint that exploits
foreground-background separation adaptively to induce the attachment of
perturbations to foregrounds. Experimentally, the proposed LGP successfully
attacked sixteen state-of-the-art object detectors on MS-COCO and DOTA
datasets, with promising imperceptibility and transferability obtained. Codes
are publicly released in https://github.com/liguopeng0923/LGP.git