Robust Image Fingerprinting Algorithm Based on Local and Global Content Dependencies

Image fingerprinting summarizes the unique visual characteristics of an image into a robust and compact ID for content identification. This technique is widely adopted by social networks to identify unauthorized uploads of copyrighted content. In this letter, we propose a deep neural network based image fingerprinting algorithm, where a neural network is designed to capture the short and long-range structural dependencies of an image and compress the representative features into fingerprints. The training algorithm optimizes the content identification accuracy of the fingerprinting model from a hypothesis-testing perspective. We propose a differentiable training objective for minimizing the error rate of the hypothesis-testing problem. Since real applications prefer binary fingerprints, we also develop an adversarial training scheme to progressively force the outputs of the neural network to approach binary states, aiming to minimize the performance loss caused by fingerprint binarization. The experimental results show that the proposed algorithm achieves more accurate content identification than state-of-theart methods and is insensitive to fingerprint binarization.