Investigating Inconsistencies in PRNU based camera identification

Nabeel Nisar Bhat, Tiziano Bianchi
Politecnico di Torino
IEEE International Conference on Image Processing, ICIP 2022
Paper Supplementary Code arXiv
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Abstract

PRNU (Photo-response non-uniformity) is widely considered a unique and reliable fingerprint for identifying the source of an image. The PRNU patterns of two different sensors, even if belonging to the same camera model, are strongly uncorrelated. Therefore, such a fingerprint is used as evidence by various law enforcement agencies for source identification, manipulation detection, etc. However, in recent smartphones, images are subjected to significant in-camera processing associated with computational photography. This heavy processing introduces non-unique artifacts (NUA) in such images and masks the uniqueness of the PRNU fingerprint. In this work, we investigate the robustness of PRNU in modern smartphones. We propose a model that explains the unexpected behavior of PRNU in such smartphones. Finally, we present two methods to identify images suffering from NUA. Our methods achieve high accuracy in identifying such images.

Key Takeaways

  • Exposing the problem of PRNU fingerprint collision in modern smartphones.
  • Unexpectedly high cross-correlation values between fingerprints belonging to two different devices.
  • This happens due to heavy processing, NUAs, that is common between different devices.
  • No link between standard meta-data settings and unexpected behavior.
  • Propose a model to explain the cause of unexpected behavior.
  • Propose two algorithms to identify images suffering from NUAs.
  • High accuracy in identifying images suffering from NUAs.

BibTeX

@inproceedings{bhat2022investigating,
  title={Investigating inconsistencies in prnu-based camera identification},
  author={Bhat, Nabeel Nisar and Bianchi, Tiziano},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={851--855},
  year={2022},
  organization={IEEE}
}