  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.
