Security and Forensics Exploration of Learning-based Image Coding

Citation

Bhowmik D, Elawady M & Nogueira K (2021) Security and Forensics Exploration of Learning-based Image Coding. In: 2021 IEEE International Conference on Visual Communications and Image Processing (VCIP). Visual Communications and Image Processing (VCIP 2021), Munich, 05.12.2021-08.12.2021. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/VCIP53242.2021.9675445

Abstract
Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG-AI) and MJoint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.

Keywords
Media forensics; security; learning based image coding; JPEG-AI; DNN; watermarking; source identification