Abstract: System and method for producing an adversarial article that may be used to disrupt an automated visual tracking process. An input module receives input related to a specific automated visual tracking process. Based on that input, a pattern-design module generates an adversarial pattern. The adversarial pattern may then be applied to an article, which may be any kind of physical or virtual object. The tracker's normal processing modes are disrupted when the tracker attempts to process an image containing the adversarial article(s). The tracker may be mounted on an autonomous vehicle, a mobile robot, or other mobile or stationary camera surveillance system.

Abstract: System and method for producing an adversarial article that may be used to disrupt an automated visual tracking process. An input module receives input related to a specific automated visual tracking process. Based on that input, a pattern-design module generates an adversarial pattern. The adversarial pattern may then be applied to an article, which may be any kind of physical or virtual object. The tracker's normal processing modes are disrupted when the tracker attempts to process an image containing the adversarial article(s). The tracker may be mounted on an autonomous vehicle, a mobile robot, or other mobile or stationary camera surveillance system.

Abstract: Methods and systems for generating adversarial examples are disclosed. The method comprises accessing a set of inputs and generating an instance of a variable auto-encoder (VAE), the instance of the VAE encoding the set of inputs into latent representation elements associated with a latent space. The method further comprises applying a manifold learning routine on the instance of the VAE to establish a characterization of a manifold in the latent space and applying a perturbation routine to generate perturbed latent representation elements while constraining the perturbed latent representation elements to remain within the manifold. The method further comprises generating adversarial examples based on the perturbed latent representation elements and outputting the adversarial examples.

Abstract: Methods and systems for generating adversarial examples are disclosed. The method comprises accessing a set of inputs and generating an instance of a variable auto-encoder (VAE), the instance of the VAE encoding the set of inputs into latent representation elements associated with a latent space. The method further comprises applying a manifold learning routine on the instance of the VAE to establish a characterization of a manifold in the latent space and applying a perturbation routine to generate perturbed latent representation elements while constraining the perturbed latent representation elements to remain within the manifold. The method further comprises generating adversarial examples based on the perturbed latent representation elements and outputting the adversarial examples.