EXPLOITING PRNU AND LINEAR PATTERNS IN FORENSIC CAMERA ATTRIBUTION UNDER COMPLEX LENS DISTORTION CORRECTION

This is the official code implementation of the "ICIP 2022" paper "EXPLOITING PRNU AND LINEAR PATTERNS IN FORENSIC CAMERA ATTRIBUTION UNDER COMPLEX LENS DISTORTION CORRECTION"

Requirements

• Download the python libraries of Camera-fingerprint ;
• if Camera-fingerprint is not already, reorganize the folders such that CameraFingerprint_python/CameraFingerprint/ ;
• Download the Reference Camera Fingerprints here;
• at least 9G GPU.

Set up Virtual-Env

conda env create -f environment.yml

Test

Test PSLR for match (H1) hypothesis cases

python -u main_OFF_H1.py

Test PSLR for match (H1) hypothesis cases on CPU

python -u main_OFF_H1_cpu.py

Test PSLR for mis-match (H0) hypothesis cases

python -u main_OFF_H0.py

NOTE:

You need to edit in main_OFF_H1.py, main_OFF_H1_cpu.py and main_OFF_H0.py:

• Fingeprint_list changing it with the path to your Camera Fingerprints
• images_set changing it with the path to the test images corresponding to your Camera Fingerprint
• outfile_name changing it with your output file name

Results of the Paper

Check "EXPLOITING PRNU AND LINEAR PATTERNS IN FORENSIC CAMERA ATTRIBUTION UNDER COMPLEX LENS DISTORTION CORRECTION"

Cite Us

If you use this code please cite:

@inproceedings{montibeller2023exploiting,
title={Exploiting PRNU and Linear Patterns in Forensic Camera Attribution under Complex Lens Distortion Correction},
author={Montibeller, Andrea and P{'e}rez-Gonz{'a}lez, Fernando},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}