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Task-generalizable Adversarial Attack based on Perceptual Metric
[article]
2019
arXiv
pre-print
Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images. These perturbed images are known as 'adversarial examples' and pose a serious threat to security and safety critical systems. A litmus test for the strength of adversarial examples is their transferability across different DNN models in a black box setting (i.e. when the target model's architecture and parameters are not known to attacker). Current attack algorithms that seek to enhance
arXiv:1811.09020v3
fatcat:2ev3legbhrgkxkigc2kcfctg6q