Digital Image Forensics using Deep Learning

TL;DR

This paper proposes a deep learning-based method to authenticate images by identifying the camera used, leveraging intrinsic image traces to improve evidence verification in legal and journalistic contexts.

Contribution

It introduces a novel algorithm combining image filters and deep neural networks to accurately determine the camera source from intrinsic image features.

Findings

Effective camera identification using deep learning

Improved accuracy over traditional metadata methods

Potential impact on legal and media evidence verification

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.