Invisible Cloak to AI Recognition from All Horizontal Directions by Adversarial Patch

Humans have a right not to be recognized by AI without permission while surveillance cameras and drones are coming to recognize humans automatically. The adversarial patch is one of the technologies that prevents objects from being recognized by AI. The patch was applied to a board to work in the real world. Moreover, it was improved to be a T-shirt with wrinkle resistance. Furthermore, it was also improved to have shooting angle resistance. However, all patches in the existing research do not have enough shooting resistance from all angles, especially from the side and from the back. Therefore, we proposed a cloak that had shooting resistance from all angles, but a pattern in a patch was sometimes misrecognized as a human. Hence, in this paper, we improved our cloak by changing the initial image to create adversarial patches. We also evaluated the recognition by AI. As a result, every pattern in a patch was no longer misrecognized as a human. On the contrary, the human detection rate increased to about 26 %, while in the previous cloak it was 1.2 %. We considered the reason for the increase to be the color of the initial image, and found a better color than the original image. We believe that a cyan initial image will make our cloak completely invisible to the AI recognition from all horizontal directions.