Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices

TL;DR

This paper introduces a holistic image manipulation detection method combining pixel co-occurrence matrices with deep learning, effectively identifying various manipulations with high accuracy and generalization across datasets.

Contribution

It presents a novel, manipulation-agnostic approach using co-occurrence matrices and CNNs, outperforming existing methods in detection accuracy and generalization.

Findings

Achieved over 0.99 AUC on training/validation datasets.

Achieved around 0.81 AUC on unseen test dataset.

Highest score among all Media Forensics Challenge 2020 participants.

Abstract

Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations. In this paper, we propose a novel approach to holistically detect tampered images using a combination of pixel co-occurrence matrices and deep learning. We extract horizontal and vertical co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Our method is agnostic to the type of manipulation and classifies an image as tampered or untampered. We train and validate our model on a dataset of more than 86,000 images. Experimental results show that our approach is promising and achieves more than 0.99 area under the curve (AUC) evaluation metric on the training and validation subsets. Further, our approach also generalizes well and achieves around 0.81 AUC on an unseen test dataset comprising more than 19,740 images released as part of the Media Forensics Challenge (MFC) 2020. Our score was highest among all other teams that participated in the challenge, at the time of announcement of the challenge results.