CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing

TL;DR

This paper introduces a CNN-based method for detecting contrast adjustments in images that remains effective even after JPEG compression, addressing challenges in forensic image analysis.

Contribution

It presents a patch-based CNN detector trained on JPEG-compressed images to identify various contrast adjustments, demonstrating robustness and scalability.

Findings

High detection accuracy across different contrast adjustments

Robust performance under various JPEG compression levels

Effective generalization to unseen tonal adjustments

Abstract

Detection of contrast adjustments in the presence of JPEG postprocessing is known to be a challenging task. JPEG post processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack, when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.