Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation. (arXiv:2303.09732v1 [cs.CR])

Copyright protection for deep neural networks (DNNs) is an urgent need for AI
corporations. To trace illegally distributed model copies, DNN watermarking is
an emerging technique for embedding and verifying secret identity messages in
the prediction behaviors or the model internals. Sacrificing less functionality
and involving more knowledge about the target DNN, the latter branch called
\textit{white-box DNN watermarking} is believed to be accurate, credible and
secure against most known watermark removal attacks, with emerging research
efforts in both the …

box called copyright corporations deep learning distributed emerging identity knowledge messages networks neural networks obfuscation prediction protection secret target trace under urgent watermarking