Review: A review on blind detection for image steganography
Signal Processing
Message estimation for universal steganalysis using multi-classification support vector machine
Computer Standards & Interfaces
A Data Mapping Method for Steganography and Its Application to Images
Information Hiding
Steganalysis of JPEG Images with Joint Transform Features
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A New Hybrid DCT and Contourlet Transform Based JPEG Image Steganalysis Technique
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
A high-capacity steganography scheme for JPEG2000 baseline system
IEEE Transactions on Image Processing
Evaluation of Feature Selection Measures for Steganalysis
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Information Sciences: an International Journal
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
Edge adaptive image steganography based on LSB matching revisited
IEEE Transactions on Information Forensics and Security
Expert Systems with Applications: An International Journal
Reliable JPEG steganalysis based on multi-directional correlations
Image Communication
A more secure steganography based on adaptive pixel-value differencing scheme
Multimedia Tools and Applications
Secure steganography using randomized cropping
Transactions on Data Hiding and Multimedia Security VII
Steganalysis of F5-like steganography based on selection of joint distribution features
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
A novel blind detector for additive noise steganography in JPEG decompressed images
Multimedia Tools and Applications
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The purpose of image steganalysis is to detect the presence of hidden messages in cover photographic images. Supervised learning is an effective and universal approach to cope with the twin difficulties of unknown image statistics and unknown steganographic codes. A crucial part of the learning process is the selection of low-dimensional informative features. We investigate this problem from three angles and propose a three-level optimization of the classifier. First, we select a subband image representation that provides better discrimination ability than a conventional wavelet transform. Second, we analyze two types of features-empirical moments of probability density functions (PDFs) and empirical moments of characteristic functions of the PDFs-and compare their merits. Third, we address the problem of feature dimensionality reduction, which strongly impacts classification accuracy. Experiments show that our method outperforms previous steganalysis methods. For instance, when the probability of false alarm is fixed at 1%, the stegoimage detection probability of our algorithm exceeds that of its closest competitor by at least 15% and up to 50%