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Stego Networks: Information Hiding on Deep Neural Networks

The best way of keeping a secret is to pretend there is not one. In this spirit, a class of techniques called steganography aims to hide secret messages on various media leaving as little detectable trace as possible. This paper considers neural networks as novel steganographic cover media, which we call stego networks, that can be used to hide one's secret messages. Although there have been numerous attempts to hide information in the output of neural networks, techniques for hiding information in the neural network parameters themselves have not been actively studied in the literature. The widespread use of deep learning models in various cloud computing platforms and millions of mobile devices as of today implies the importance of safety issues regarding stego networks among deep learning researchers and practitioners. In response, this paper presents the advantages of stego networks over other types of stego media in terms of security and capacity. We provide observations that the fraction bits of some typical network parameters in a floating-point representation tend to follow uniform distributions and explain how it can help a secret sender to encrypt messages that are indistinguishable from the original content. We demonstrate that network parameters can embed a large amount of secret information. Even the most significant fraction bits can be used for hiding secrets without inducing noticeable performance degradation while making it significantly hard to remove secrets by perturbing insignificant bits. Finally, we discuss possible use cases of stego networks and methods to detect or remove secrets from stego networks.

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