An introduction to the Ising model
American Mathematical Monthly
Fundamentals of digital image processing
Fundamentals of digital image processing
Data mining: concepts and techniques
Data mining: concepts and techniques
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Attacks on Steganographic Systems
IH '99 Proceedings of the Third International Workshop on Information Hiding
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Information Hiding: Steganography and Watermarking-Attacks and Countermeasures Steganography and Watermarking - Attacks and Countermeasures
Identifying steganographic payload location in binary image
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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Steganography involves hiding messages in innocuous media such as images, while steganalysis is the field of detecting these secret messages. The ultimate goal of steganalysis is two-fold: making a binary classification of a file as stego-bearing or innocent, and secondly, locating the hidden message with an aim to extracting, sterilizing or manipulating it. Almost all steganalysis approaches (known as attacks) focus on the first of these two issues. In this paper, we explore the difficult related problem: given that we know an image file contains steganography, locate which pixels contain the message. We treat the hidden message location problem as outlier detection using probability/energy measures of images motivated by the image restoration community. Pixels contributing the most to the energy calculations of an image are deemed outliers. Typically, of the top third of one percent of most energized pixels (outliers), we find that 87% are stego-bearing in color images and 61% in grayscale images. In all image types only 1% of all pixels are stego-bearing indicating our techniques provides a substantial lift over random guessing.