INFOPRINT: INFORMATION THEORETIC DIGITAL IMAGE FORENSICS

Tampered images pose a serious predicament since digitized media is a ubiquitous part of our lives. These are facilitated by the availability of image editing software and recent advances in deep Generative Adversarial Networks (GANs). We propose an innovative method to formulate the problem of localizing manipulated regions in fake images as a deep representation learning problem using the Information Bottleneck (IB) principle. We devise a convolutional neural net-based architecture, InfoPrint (IP), that uses variational inference to approximate the IB formulation. Testing on three standard datasets, we demonstrate that InfoPrint outperforms the state- of-the-art by 3% points or more. Additionally, we demonstrate that it has the ability to to detect alterations made by inpainting GANs.