Robust and Secure Image Fingerprinting Learned by Neural Network

Image fingerprinting is a technique that summarizes the perceptual characteristics of a digital image into an invariant digest, and it is one of the most effective solutions for digital rights management. Most conventional fingerprinting algorithms were developed by assembling manually designed feature extractor and quantizer, which requires extensive expert knowledge and may not capture the intrinsic or abstract visual characteristics of the digital image. Focusing on content identification related applications, we propose a data-driven image fingerprinting algorithm in this paper, where neural network is trained to automatically discover the optimal mapping from image to fingerprint. To ameliorate the difficulty of training, we start by training the fingerprint-computation network in a layer-wise manner to progressively improve its robustness against content-preserving distortions. Initialized by the states learned by layer-wise training, the network is then re-trained as a holistic unit, with the objective of maximizing its content identification accuracy. Moreover, we also develop a key-dependent version of the neural network-based fingerprinting algorithm. By quantifying its security using information-theoretic metrics, we have proved that the hierarchical architecture of neural network is beneficial to the security of fingerprinting algorithm. The experimental results on a large testing database show that the proposed work exhibits much higher content identification accuracy than state-of-the-art algorithms, and its execution speed is in the millisecond time scale.