Neural Imaging Pipelines - the Scourge or Hope of Forensics?
Pawel Korus, Nasir Memon

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…
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Taxonomy
TopicsDigital Media Forensic Detection · Cell Image Analysis Techniques · Generative Adversarial Networks and Image Synthesis
