ACL: Account Linking in Online Social Networks With Robust Camera Fingerprint Matching

Liu Liu, Xinwen Fu, Xiaodong Chen, Jianpeng Wang, Zhongjie Ba, Feng Lin, Li Lu, Kui Ren

Abstract: Pseudonyms used in Online social networks (OSNs) post a great challenge to fighting against cyber crimes. To build a strong case, law enforcement may want to link multiple user accounts with pseudonyms to a physical suspect. Images based camera fingerprinting has been used for account linking when a suspect takes pictures and videos with his camera and posts them online. However, image post-processing software may introduce noise into images. This noise is hard to eliminate by conventional strategies, is partly resident in the estimated photo-response non-uniformity (PRNU) fingerprints, and interferes with matching fingerprints. We define this noise as software noise, which pollutes PRNU fingerprints and affects accounts linking in online social networks. In this article, we propose new approaches for camera fingerprint matching given software noise. The key idea is to determine the PRNU hardware noise correlation component with our new test statistic–fingerprint to software noise ratio (FITS). We performed extensive experiments and 10,000+ images taken by 90+ smartphones were used to validate our robust camera fingerprint matching system. The experiment results show FITS outperforms the state-of-the-art approaches for polluted fingerprints. This is the first work studying camera fingerprint matching with the presence of software noise.

External IDs:dblp:journals/tdsc/LiuFCWBLLR25