TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems. (arXiv:2111.09999v2 [cs.CV] UPDATED)

Deep neural networks are vulnerable to attacks from adversarial inputs and,
more recently, Trojans to misguide or hijack the model's decision. We expose
the existence of an intriguing class of spatially bounded, physically
realizable, adversarial examples -- Universal NaTuralistic adversarial paTches
-- we call TnTs, by exploring the superset of the spatially bounded adversarial
example space and the natural input space within generative adversarial
networks. Now, an adversary can arm themselves with a patch that is
naturalistic, less malicious-looking, physically …

adversarial attacks network neural network patches systems