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
Blind Image Watermark Analysis Using Feature Fusion and Neural Network Classifier
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
StegErmelc: A Novel DCT-Based Steganographic Method Using Three Strategies
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Evaluation of Feature Selection Measures for Steganalysis
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
A wavelet-based blind JPEG image steganalysis uing co-occurrence matrix
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
Information Sciences: an International Journal
Classification of steganalysis techniques: A study
Digital Signal Processing
Expert Systems with Applications: An International Journal
Reliable JPEG steganalysis based on multi-directional correlations
Image Communication
Steganalysis of LSB matching based on statistical modeling of pixel difference distributions
Information Sciences: an International Journal
Passive steganalysis based on higher order image statistics of curvelet transform
International Journal of Automation and Computing
Steganalysis of LSB matching based on the statistical analysis of empirical matrix
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Parameter-estimation and algorithm-selection based United-Judgment for image steganalysis
Multimedia Tools and Applications
A Comparative Survey on Cryptology-Based Methodologies
International Journal of Information Security and Privacy
| Hi-index | 0.00 |
In contrast to steganography, steganalysis is focused on detecting (the main goal of this research), tracking, extracting, and modifying secret messages transmitted through a covert channel. In this paper, a feature classification technique, based on the analysis of two statistical properties in the spatial and DCT domains, is proposed to blindly (i.e., without knowledge of the steganographic schemes) to determine the existence of hidden messages in an image. To be effective in class separation, the nonlinear neural classifier was adopted. For evaluation, a database composed of 2088 plain and stego images (generated by using six different embedding schemes) was established. Based on this database, extensive experiments were conducted to prove the feasibility and diversity of our proposed system. It was found that the proposed system consists of: 1) a 90%+ positive-detection rate; 2) not limited to the detection of a particular steganographic scheme; 3) capable of detecting stego images with an embedding rate as low as 0.01 bpp; and 4) considering the test of plain images incurred low-pass filtering, sharpening, and JPEG compression.