# Localization of JPEG double compression through multi-domain convolutional neural networks

## TL;DR

This paper explores the use of multi-domain convolutional neural networks to detect and localize JPEG double compression, advancing image forgery detection techniques with machine learning.

## Contribution

It introduces a CNN-based approach for localizing JPEG double compression, considering various input types and analyzing potential issues for forensic image analysis.

## Key findings

- Effective localization of double JPEG compression achieved
- Different input types influence CNN performance
- Identified challenges for future research in image forensics

## Abstract

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.