Web: http://arxiv.org/abs/2211.12005

Nov. 23, 2022, 2:20 a.m. | Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong, Minghai Qin, Yanzhi Wang, Xiaolin Huang

cs.CR updates on arXiv.org arxiv.org

As data become increasingly vital for deep learning, a company would be very
cautious about releasing data, because the competitors could use the released
data to train high-performance models, thereby posing a tremendous threat to
the company's commercial competence. To prevent training good models on the
data, imperceptible perturbations could be added to it. Since such
perturbations aim at hurting the entire training process, they should reflect
the vulnerability of DNN training, rather than that of a single model. Based …

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