IEEE Transactions on Pattern Analysis and Machine Intelligence
IHW '01 Proceedings of the 4th 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
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Image steganalysis with binary similarity measures
EURASIP Journal on Applied Signal Processing
Score normalization in multimodal biometric systems
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IH'04 Proceedings of the 6th international conference on Information Hiding
Fusion Based Blind Image Steganalysis by Boosting Feature Selection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Pros and cons of mel-cepstrum based audio steganalysis using SVM classification
IH'07 Proceedings of the 9th international conference on Information hiding
Parameter-estimation and algorithm-selection based United-Judgment for image steganalysis
Multimedia Tools and Applications
Measuring the statistical correlation inconsistencies in mobile images for tamper detection
Transactions on Data Hiding and Multimedia Security VII
A fuzzy approach to deal with uncertainty in image forensics
Image Communication
Information Sciences: an International Journal
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In the past few years, we have witnessed a number of powerful steganalysis technique proposed in the literature. These techniques could be categorized as either specific or universal. Each category of techniques has a set of advantages and disadvantages. A steganalysis technique specific to a steganographic embedding technique would perform well when tested only on that method and might fail on all others. On the other hand, universal steganalysis methods perform less accurately overall but provide acceptable performance in many cases. In practice, since the steganalyst will not be able to know what steganographic technique is used, it has to deploy a number of techniques on suspected images. In such a setting the most important question that needs to be answered is: What should the steganalyst do when the decisions produced by different steganalysis techniques are in contradiction? In this work, we propose and investigate the use of information fusion methods to aggregate the outputs of multiple steganalysis techniques. We consider several fusion rules that are applicable to steganalysis, and illustrate, through a number of case studies, how composite steganalyzers with improved performance can be designed. It is shown that fusion techniques increase detection accuracy and offer scalability, by enabling seamless integration of new steganalysis techniques.