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Quanxin Zhang

2 accepted papers

2022

Enhancing the Transferability of Adversarial Examples with Random Patch

IJCAI 2022poster

Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features of objects to generate adversarial examples. This paper prop…

2021

Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity

IJCAI 2021poster

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples. Adversarial examples are malicious images with visually imperceptible perturbations. While these carefully crafted perturbations restricted with tight Lp norm bounds are small, they are still easily perceivable by…

Cited by 35SourcePDFScholar