Adversarial Frontier Stitching for Remote Neural Network Watermarking

6 Nov 2017Erwan Le MerrerPatrick PerezGilles Trédan

The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Uchida et al. (2017) proposed to watermark convolutional neural networks by embedding information into their weights... (read more)

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