Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations
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).
BibTeX
@inproceedings{
Korus2020Quantifying,
title={Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations},
author={Pawel Korus and Nasir Memon},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HyxG3p4twS}
}