SP ATTACK: SINGLE-PERSPECTIVE ATTACK FOR GENERATING ADVERSARIAL OMNIDIRECTIONAL IMAGES

The safety of Deep Neural Networks (DNNs) processing omnidirectional images (ODIs) is an under-researched topic. In this paper, we propose a novel sparse attack, named Single-Perspective (SP) Attack, towards fooling these models by perturbing only one perspective image (PI) rendered from the target ODI. The attack is launched from the perspective domain, and finally the perturbation is transferred to the original ODI. To this end, we propose an effective PI position searching algorithm based on Bayesian Optimization, and then corrupt the PI centered on the desirable position with unconstrained/constrained perturbations. Extensive experiments on synthetic and real-world omnidirectional datasets demonstrate that SP Attack can overcome the projection deformation of ODIs, and mislead the neural networks by limiting the perturbations in a single patch on the target ODI.