Jul 17, 2020
Engineering a top-notch deep neural network (DNN) is an expensive procedure which involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources. For that reason, DNNs are considered as valuable Intellectual Properties (IPs) of the model vendors. To ensure a reliable commercialization of these products, it is crucial to develop techniques to protect model vendors against IP infringements. One of such techniques that recently has shown great promise is digital watermarking. In this paper, we present GradSigns, a novel watermarking framework for DNNs. GradSigns embeds owner's signature into gradient of cross-entropy cost function with respect to inputs to the model. Our approach has negligible impact on the performance of the protected model, and can verify ownership of remotely deployed models through prediction APIs. We evaluate GradSigns on DNNs trained for different image classification tasks using CIFAR-10, SVHN and YTF datasets, and experimentally show that unlike existing methods, GradSigns is robust against counter-watermark attacks, and can embed large amount of information into DNNs.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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