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AdvJND: Generating Adversarial Examples with Just Noticeable Difference
To alleviate the tradeoff between the attack success rate and image fidelity, we propose a method named AdvJND, adding visual model coefficients, just noticeable difference coefficients, in the constraint of a distortion function when generating adversarial examples.
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
The proposed model comprising a texture transfer network (TTN) and an auxiliary defense generative adversarial networks (GAN) is called Human-perception Auxiliary Defense GAN (HAD-GAN).
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them.
