How good are convex hull algorithms?
Proceedings of the eleventh annual symposium on Computational geometry
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Principles of a computer immune system
NSPW '97 Proceedings of the 1997 workshop on New security paradigms
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Information Hiding Techniques for Steganography and Digital Watermarking
Information Hiding Techniques for Steganography and Digital Watermarking
Steganalysis of JPEG Images: Breaking the F5 Algorithm
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Hide and Seek: An Introduction to Steganography
IEEE Security and Privacy
Least significant bit steganography detection with machine learning techniques
Proceedings of the 2007 international workshop on Domain driven data mining
Review: A review on blind detection for image steganography
Signal Processing
A New Hybrid DCT and Contourlet Transform Based JPEG Image Steganalysis Technique
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Classification of steganalysis techniques: A study
Digital Signal Processing
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Steganography is the art of hiding a message in plain sight. Modern steganographic tools that conceal data in innocuous-looking digital image files are widely available. The use of such tools by terrorists, hostile states, criminal organizations, etc., to camouflage the planning and coordination of their illicit activities poses a serious challenge. Most steganography detection tools rely on signatures that describe particular steganography programs. Signature-based classifiers offer strong detection capabilities against known threats, but they suffer from an inability to detect previously unseen forms of steganography. Novel steganography detection requires an anomaly-based classifier. This paper describes and demonstrates a blind classification algorithm that uses hyper-dimensional geometric methods to model steganography-free jpeg images. The geometric model, comprising one or more convex polytopes, hyper-spheres, or hyper-ellipsoids in the attribute space, provides superior anomaly detection compared to previous research. Experimental results show that the classifier detects, on average, 85.4% of Jsteg steganography images with a mean embedding rate of 0.14 bits per pixel, compared to previous research that achieved a mean detection rate of just 65%. Further, the classification algorithm creates models for as many training classes of data as are available, resulting in a hybrid anomaly/signature or signature-only based classifier, which increases Jsteg detection accuracy to 95%.