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Adversarial frontier stitching for remote neural network watermarking

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The state-of-the-art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Nagai et al. (Int J Multimed Inf Retr 7(1):3–16, 2018), Uchida et al. (Embedding watermarks into deep neural networks, ICMR, 2017) proposed to watermark convolutional neural networks for image classification, by embedding information into their weights. While this is a clear progress toward model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to mark the model’s action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. In the present paper, we formally introduce the problem and propose a novel zero-bit watermarking algorithm that makes use of adversarial model examples. While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few queries. We experimented the approach on three neural networks designed for image classification, in the context of MNIST digit recognition task.

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  1. \({\hat{k}}_w+\varepsilon\)” stands for a small modification of the parameters of \({\hat{k}}_w\) that preserves the value of the model, i.e., that does not deteriorate significantly its performance.

  2. Code will be open-sourced on GitHub, upon article acceptance.

  3. This about \(3.5\%\) accuracy drop is also the one tolerated by a recent work on trojaning neural networks [18].



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The authors would like to thank the reviewers for their constructive comments.

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Correspondence to Erwan Le Merrer.

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Le Merrer, E., Pérez, P. & Trédan, G. Adversarial frontier stitching for remote neural network watermarking. Neural Comput & Applic 32, 9233–9244 (2020).

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