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Towards Robust Classification with Image Quality Assessment
[article]
2020
arXiv
pre-print
Recent studies have shown that deep convolutional neural networks (DCNN) are vulnerable to adversarial examples and sensitive to perceptual quality as well as the acquisition condition of images. These findings raise a big concern for the adoption of DCNN-based applications for critical tasks. In the literature, various defense strategies have been introduced to increase the robustness of DCNN, including re-training an entire model with benign noise injection, adversarial examples, or adding
arXiv:2004.06288v1
fatcat:ehtc4qyv5vgefpw7okunkzabni