This paper considers the research goal of dependable steganalysis: where false positives occur once in a million or less, and this rate is known with high precision. Despite its importance for real-world application, there has been almost no study of steganalysis which produces very low false positives. We test existing and novel classifiers for their low false-positive performance, using millions of images from Flickr. Experiments on such a scale require considerable engineering. Standard steganalysis classifiers do not perform well in a low false-positive regime, and we make new proposals to penalize false positives more than false negatives.

Date Published: 4 March 2015
PDF: 14 pages
Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090I (4 March 2015); doi: 10.1117/12.2083216

Published in SPIE Proceedings Vol. 9409:
Media Watermarking, Security, and Forensics 2015
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)