  With the growing popularity of smartphone photography in recent years, web
photos play an increasingly important role in all walks of life. Source camera
identification of web photos aims to establish a reliable linkage from the
captured images to their source cameras, and has a broad range of applications,
such as image copyright protection, user authentication, investigated evidence
verification, etc. This paper presents an innovative and practical source
identification framework that employs neural-network enhanced sensor pattern
noise to trace back web photos efficiently while ensuring security. Our
proposed framework consists of three main stages: initial device fingerprint
registration, fingerprint extraction and cryptographic connection establishment
while taking photos, and connection verification between photos and source
devices. By incorporating metric learning and frequency consistency into the
deep network design, our proposed fingerprint extraction algorithm achieves
state-of-the-art performance on modern smartphone photos for reliable source
identification. Meanwhile, we also propose several optimization sub-modules to
prevent fingerprint leakage and improve accuracy and efficiency. Finally for
practical system design, two cryptographic schemes are introduced to reliably
identify the correlation between registered fingerprint and verified photo
fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for
fingerprint extraction network and benchmark dataset with modern smartphone
cameras photos are all publicly available at
https://github.com/PhotoNecf/PhotoNecf.
