Protecting artificial intelligence IPs: a survey of watermarking and fingerprinting for machine learning
Institution of Engineering and Technology (IET)
Artificial intelligence (AI) algorithms achieve outstanding results in many application domains such as computer vision and natural language processing. The performance of AI models is the outcome of complex and costly model architecture design and training processes. Hence, it is paramount for model owners to protect their AI models from piracy – model cloning, illegitimate distribution and use. IP protection mechanisms have been applied to AI models, and in particular to deep neural networks, to verify the model ownership. State-of-the-art AI model ownership protection techniques have been surveyed. The pros and cons of AI model ownership protection have been reported. The majority of previous works are focused on watermarking, while more advanced methods such fingerprinting and attestation are promising but not yet explored in depth. This study has been concluded by discussing possible research directions in the area.
Artificial intelligence , AI models , Piracy , IP protection mechanisms
Regazzoni, F., Palmieri, P., Smailbegovic, F., Cammarota, R. and Polian, I. (2021) 'Protecting artificial intelligence IPs: a survey of watermarking and fingerprinting for machine learning', CAAI Transactions on Intelligence Technology, 6(2), pp. 180-191. doi: 10.1049/cit2.12029
© 2021, the Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of the Institution of Engineering and Technology and Chongqing University of Technology. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.