Neural Imaging Pipelines - the Scourge or Hope of Forensics?

TL;DR

This paper proposes a neural imaging pipeline that enhances forensic analysis by embedding detectable artifacts during image processing, significantly improving manipulation detection accuracy from 45% to over 90%.

Contribution

It introduces a neural network-based imaging pipeline optimized for both high-quality image rendering and reliable provenance analysis, a novel approach in digital forensics.

Findings

Manipulation detection accuracy increased from 45% to over 90%.

The neural pipeline introduces artifacts similar to digital watermarks.

Most performance gains are achieved with minor image distortion.

Abstract

Forensic analysis of digital photographs relies on intrinsic statistical traces introduced at the time of their acquisition or subsequent editing. Such traces are often removed by post-processing (e.g., down-sampling and re-compression applied upon distribution in the Web) which inhibits reliable provenance analysis. Increasing adoption of computational methods within digital cameras further complicates the process and renders explicit mathematical modeling infeasible. While this trend challenges forensic analysis even in near-acquisition conditions, it also creates new opportunities. This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel, where state-of-the-art forensic techniques fail. We demonstrate that a neural network can be trained to replace the entire photo development pipeline, and jointly optimized for high-fidelity photo rendering and reliable provenance analysis. Such optimized neural imaging pipeline allowed us to increase image manipulation detection accuracy from approx. 45% to over 90%. The network learns to introduce carefully crafted artifacts, akin to digital watermarks, which facilitate subsequent manipulation detection. Analysis of performance trade-offs indicates that most of the gains can be obtained with only minor distortion. The findings encourage further research towards building more reliable imaging pipelines with explicit provenance-guaranteeing properties.