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Exploring Adversarial Attacks on Neural Networks: An Explainable Approach. (arXiv:2303.06032v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
Deep Learning (DL) is being applied in various domains, especially in
safety-critical applications such as autonomous driving. Consequently, it is of
great significance to ensure the robustness of these methods and thus
counteract uncertain behaviors caused by adversarial attacks. In this paper, we
use gradient heatmaps to analyze the response characteristics of the VGG-16
model when the input images are mixed with adversarial noise and statistically
similar Gaussian random noise. In particular, we compare the network response
layer by layer …
adversarial adversarial attacks applications attacks autonomous autonomous driving critical deep learning domains driving great images input networks neural networks random response robustness safety safety-critical