Digital Image Forensics using Deep Learning
Abstract
During the investigation of criminal activity when evidence is available, the issue at hand is determining the credibility of the video and ascertaining that the video is real. Today, one way to authenticate the footage is to identify the camera that was used to capture the image or video in question. While a very common way to do this is by using image meta-data, this data can easily be falsified by changing the video content or even splicing together content from two different cameras. Given the multitude of solutions proposed to this problem, it is yet to be sufficiently solved. The aim of our project is to build an algorithm that identifies which camera was used to capture an image using traces of information left intrinsically in the image, using filters, followed by a deep neural network on these filters. Solving this problem would have a big impact on the verification of evidence used in criminal and civil trials and even news reporting.
Cite
@article{arxiv.2210.09052,
title = {Digital Image Forensics using Deep Learning},
author = {Akash Nagaraj and Mukund Sood and Vivek Kapoor and Yash Mathur and Bishesh Sinha},
journal= {arXiv preprint arXiv:2210.09052},
year = {2022}
}
Comments
This paper was written in 2018 as a part of our submission to the 2018 IEEE Signal Processing Cup: Forensic Camera Model Identification Challenge