Unsupervised Energy-Based Model for the Identification of Out-Of-Distribution Copy Detection Patterns

The recent advances in deep neural networks have led us to revisit the safety of current mechanisms for the authentification of people’s identities and validating the provenance of traded goods. This paper studies the problem of identifying counterfeits of Copy Detection Patterns (CDPs). CDPs are used on product packaging to identify counterfeiting by relying on the information loss principle. Current verification techniques rely on a supervised machine learning paradigm, which needs examples from real and fake CDPs to learn a model capable of differentiating both classes of CDPs. This paper proposes an unsupervised forensic approach that is capable of training an Energy-Based Model for detecting out-of-distribution (OOD) images (fake CDP examples in our case) using only original CDP images. This paper also introduces a novel thresholding technique that only requires original CDPs for threshold value selection. We validate our approach on the Indigo dataset, with results demonstrating comparable counterfeit detection capabilities to prior work but using a method trained on less data.