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).
Cite
Text
Korus and Memon. "Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations." International Conference on Learning Representations, 2020.Markdown
[Korus and Memon. "Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations." International Conference on Learning Representations, 2020.](https://mlanthology.org/iclr/2020/korus2020iclr-quantifying/)BibTeX
@inproceedings{korus2020iclr-quantifying,
title = {{Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations}},
author = {Korus, Pawel and Memon, Nasir},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://mlanthology.org/iclr/2020/korus2020iclr-quantifying/}
}