June 6, 2024, 4:49 a.m. | Zhixun He, Mukesh Singhal

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.03117v1 Announce Type: new
Abstract: Deep Neural Networks (DNN) have become a promising paradigm when developing Artificial Intelligence (AI) and Machine Learning (ML) applications. However, DNN applications are vulnerable to fake data that are crafted with adversarial attack algorithms. Under adversarial attacks, the prediction accuracy of DNN applications suffers, making them unreliable. In order to defend against adversarial attacks, we introduce a novel noise-reduction procedure, Vector Quantization U-Net (VQUNet), to reduce adversarial noise and reconstruct data with high fidelity. VQUNet …

abstract accuracy adversarial adversarial attacks algorithms applications artificial artificial intelligence arxiv attacks become cs.cv data dnn fake fake data however intelligence machine machine learning networks neural networks noise paradigm prediction quantization type vector vulnerable

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