PRNU Fingerprint Purification Algorithm for Open Environment

Non-unique artifacts(NUAs) noise generated by digital image post-processing pipeline is mixed in unique and stable photo response non-uniformity(PRNU) fingerprints.It seriously affects the precision of the downstream source camera identification(SCI) task.However,existing NUAs suppression schemes mainly target experimental environments and require not only additional hyperparameter settings,but also additional computing resources and storage space,which are difficult to apply in open environments.To solve this problem,this paper proposes a PRNU fingerprint purification algorithm for open environments.Firstly,it improves the existing PRNU fingerprint correlation metric peak-to-correlation energy ratio(PCE) and proposes PCE_norm and PCE_denuas based on normalization to achieve adaptive correlation measurement in open environment.Then,NUAs offline suppression is realized by constructing a contrastive learning mechanism to reduce the distance of the same fingerprint and amplify different fingerprints,so that there is no need for additional computation and storage costs for online suppression in SCI tasks.Finally,experiments on Dresden and Daxing datasets demonstrate the effectiveness and robustness of the proposed algorithm.