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Deep Learning (DL) models have caused a paradigm shift in our ability tocomprehend raw data in various important fields, ranging from intelligencewarfare and healthcare to autonomous transportation and automatedmanufacturing. A practical concern, in the rush to adopt DL models as aservice, is protecting the models against Intellectual Property (IP)infringement. The DL models are commonly built by allocating significantcomputational resources that process vast amounts of proprietary training data.The resulting models are therefore considered to be the IP of the model builderand need to be protected to preserve the owner's competitive advantage.  This paper proposes DeepSigns, a novel end-to-end IP protection frameworkthat enables insertion of coherent digital watermarks in contemporary DLmodels. DeepSigns, for the first time, introduces a generic watermarkingmethodology that can be used for protecting DL owner's IP rights in bothwhite-box and black-box settings, where the adversary may or may not have theknowledge of the model internals. The suggested methodology is based onembedding the owner's signature (watermark) in the probability density function(pdf) of the data abstraction obtained in different layers of a DL model.DeepSigns can demonstrably withstand various removal and transformationattacks, including model compression, model fine-tuning, and watermarkoverwriting. Proof-of-concept evaluations on MNIST, and CIFAR10 datasets, aswell as a wide variety of neural network architectures including Wide ResidualNetworks, Convolution Neural Networks, and Multi-Layer Perceptrons corroborateDeepSigns' effectiveness and applicability.

DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models