Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image Classification and Object Detection

It is well known that query-based attacks tend to have relatively higher success rates in adversarial black-box attacks. While research on black-box attacks is actively being conducted, relatively few studies have focused on pixel attacks that target only a limited number of pixels. In image classification, query-based pixel attacks often rely on patches, which heavily depend on randomness and neglect the fact that scattered pixels are more suitable for adversarial attacks. Moreover, to the best of our knowledge, query-based pixel attacks have not been explored in the field of object detection. To address these issues, we propose a novel pixel-based black-box attack called R emember and Forget Pixel A ttack using R einforcement Learning(RFP AR), consisting of two main components: the Remember and Forget processes.