Floating search methods in feature selection
Pattern Recognition Letters
Detecting LSB Steganography in Color and Gray-Scale Images
IEEE MultiMedia
Steganography in Digital Media: Principles, Algorithms, and Applications
Steganography in Digital Media: Principles, Algorithms, and Applications
Steganalysis by subtractive pixel adjacency matrix
IEEE Transactions on Information Forensics and Security
SVD-based universal spatial domain image steganalysis
IEEE Transactions on Information Forensics and Security
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
Blind statistical steganalysis of additive steganography using wavelet higher order statistics
CMS'05 Proceedings of the 9th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
On dangers of overtraining steganography to incomplete cover model
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
Steganalysis of DCT-embedding based adaptive steganography and YASS
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
"Break our steganographic system": the ins and outs of organizing BOSS
IH'11 Proceedings of the 13th international conference on Information hiding
Steganalysis with mismatched covers: do simple classifiers help?
Proceedings of the on Multimedia and security
Steganalysis of LSB replacement using parity-aware features
IH'12 Proceedings of the 14th international conference on Information Hiding
Textural features for steganalysis
IH'12 Proceedings of the 14th international conference on Information Hiding
A novel mapping scheme for steganalysis
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
| Hi-index | 0.00 |
This paper presents a new methodology for the steganalysis of digital images. In principle, the proposed method is applicable to any kind of steganography at any domain. Special interest is put on the steganalysis of Highly Undetectable Steganography (HUGO). The proposed method first extracts features via applying a function to the image, constructing the k variate probability density function (PDF) estimates, and downsampling it by a suitable downsampling algorithm. The extracted feature vectors are then further optimized in order to increase the detection performance and reduce the computational time. Finally using a supervised classification algorithm such as SVM, steganalysis is performed. The proposed method is capable of detecting BOSSRank image set with an accuracy of 85%.