To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics

TL;DR

This paper introduces InfoPrint, a novel deep representation learning method based on the Information Bottleneck framework, to localize manipulated regions in digital images, outperforming existing methods and detecting GAN-based alterations.

Contribution

It formulates image forensics as an IB-based deep learning problem and proposes an efficient variational inference solution called InfoPrint, improving detection accuracy over prior approaches.

Findings

InfoPrint outperforms state-of-the-art methods on standard datasets.

It effectively detects inpainting GAN manipulations.

The variational inference approach is computationally efficient.

Abstract

We consider an information theoretic approach to address the problem of identifying fake digital images. We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks. Tampered images pose a serious predicament since digitized media is a ubiquitous part of our lives. These are facilitated by the easy availability of image editing software and aggravated by recent advances in deep generative models such as GANs. We propose InfoPrint, a computationally efficient solution to the IB formulation using approximate variational inference and compare it to a numerical solution that is computationally expensive. Testing on a number of standard datasets, we demonstrate that InfoPrint outperforms the state-of-the-art and the numerical solution. Additionally, it also has the ability to detect alterations made by inpainting GANs.