L-PRNU: Low-Complexity Privacy-Preserving PRNU-Based Camera Attribution Scheme

Abstract

A personal camera fingerprint can be created from images in social media by using Photo Response Non-Uniformity (PRNU) noise, which is used to identify whether an unknown picture belongs to them. Social media has become ubiquitous in recent years and many of us regularly share photos of our daily lives online. However, due to the ease of creating a PRNU-based camera fingerprint, the privacy leakage problem is taken more seriously. To address this issue, a security scheme based on Boneh–Goh–Nissim (BGN) encryption was proposed in 2021. While effective, the BGN encryption incurs a high run-time computational overhead due to its power computation. Therefore, we devised a new scheme to address this issue, employing polynomial encryption and pixel confusion methods, resulting in a computation time that is over ten times faster than BGN encryption. This eliminates the need to only send critical pixels to a Third-Party Expert in the previous method. Furthermore, our scheme does not require decryption, as polynomial encryption and pixel confusion do not alter the correlation value. Consequently, the scheme we presented surpasses previous methods in both theoretical analysis and experimental performance, being faster and more capable.