Fingerprinting Deep Image Restoration Models
Fingerprinting is a promising non-invasive method for protecting the intellectual property rights (IPR) of deep neural network (DNN) models. It extracts a feature called a fingerprint from a DNN model to identify its ownership. Existing fingerprinting methods focus only on classification-related models that map images to labels, while inapplicable to models for image restoration that map images to images. This paper proposes a fingerprinting framework for DNN models of image restoration. The proposed framework defines the fingerprint using a critical image, which exhibits strongly discriminative patterns and is robust to modest model modifications. Model ownership is then verified by comparing the distance of color histograms and local gradient pattern histograms of critical images between the suspect and source models. We apply the proposed framework to two representative tasks, denoising and super-resolution. It outperforms the baselines of fingerprinting and competes against existing invasive model watermarking methods.
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