19 February 2014 A mishmash of methods for mitigating the model mismatch mess
Author Affiliations +
Abstract
The model mismatch problem occurs in steganalysis when a binary classifier is trained on objects from one cover source and tested on another: an example of domain adaptation. It is highly realistic because a steganalyst would rarely have access to much or any training data from their opponent, and its consequences can be devastating to classifier accuracy. This paper presents an in-depth study of one particular instance of model mismatch, in a set of images from Flickr using one fixed steganography and steganalysis method, attempting to separate different effects of mismatch in feature space and find methods of mitigation where possible. We also propose new benchmarks for accuracy, which are more appropriate than mean error rates when there are multiple actors and multiple images, and consider the case of 3-valued detectors which also output `don't know'. This pilot study demonstrates that some simple feature-centering and ensemble methods can reduce the mismatch penalty considerably, but not completely remove it.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew D. Ker, Andrew D. Ker, Tomáš Pevný, Tomáš Pevný, } "A mishmash of methods for mitigating the model mismatch mess", Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, 90280I (19 February 2014); doi: 10.1117/12.2038908; https://doi.org/10.1117/12.2038908
PROCEEDINGS
15 PAGES


SHARE
RELATED CONTENT

Exploring multitask learning for steganalysis
Proceedings of SPIE (March 22 2013)
The challenges of rich features in universal steganalysis
Proceedings of SPIE (March 22 2013)
Towards dependable steganalysis
Proceedings of SPIE (March 04 2015)
Identification of simple objects in image sequences
Proceedings of SPIE (August 17 1994)

Back to Top