Enhancing Remote Adversarial Patch Attacks on Face Detectors with Tiling and Scaling
This work addresses security vulnerabilities in face detection systems, presenting an incremental improvement over existing adversarial attack methods.
The paper tackled the challenge of Remote Adversarial Patch attacks on face detectors by addressing issues like scale variation and class gap, proposing new patch placement and loss functions that showed superior detection obstruction effects compared to general object detector patches.
This paper discusses the attack feasibility of Remote Adversarial Patch (RAP) targeting face detectors. The RAP that targets face detectors is similar to the RAP that targets general object detectors, but the former has multiple issues in the attack process the latter does not. (1) It is possible to detect objects of various scales. In particular, the area of small objects that are convolved during feature extraction by CNN is small,so the area that affects the inference results is also small. (2) It is a two-class classification, so there is a large gap in characteristics between the classes. This makes it difficult to attack the inference results by directing them to a different class. In this paper, we propose a new patch placement method and loss function for each problem. The patches targeting the proposed face detector showed superior detection obstruct effects compared to the patches targeting the general object detector.