Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations

Pawel Korus, Nasir Memon

Keywords: image forensics, photo manipulation detection, learned compression, lossy compression, image compression, entropy estimation

TL;DR: We learn an efficient lossy image codec that can be optimized to facilitate reliable photo manipulation detection at fractional cost in payload/quality and even at low bitrates.

Abstract: Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online. We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives. We design a lightweight trainable lossy image codec, that delivers competitive rate-distortion performance, on par with best hand-engineered alternatives, but has lower computational footprint on modern GPU-enabled platforms. Our results show that significant improvements in manipulation detection accuracy are possible at fractional costs in bandwidth/storage. Our codec improved the accuracy from 37% to 86% even at very low bit-rates, well below the practicality of JPEG (QF 20).

Code: https://github.com/pkorus/neural-imaging