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

Yanwei Liu

Abstract:

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 SinglePerspective (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