Back to Search
Start Over
Steganalysis Over Large-Scale Social Networks With High-Order Joint Features and Clustering Ensembles
- Source :
- IEEE Transactions on Information Forensics and Security. 11:344-357
- Publication Year :
- 2016
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2016.
-
Abstract
- This paper tackles a recent challenge in identifying culprit actors, who try to hide confidential payload with steganography, among many innocent actors in social media networks. The problem is called steganographer detection problem and is significantly different from the traditional stego detection problem that classifies an individual object as a cover or a stego. To solve the steganographer detection problem over large-scale social media networks, this paper proposes a method that uses high-order joint features and clustering ensembles. It employs 250-D features calculated from the high-order joint matrices of Discrete Cosine Transform (DCT) coefficients of JPEG images, which indicate the dependencies of image content. Furthermore, a number of hierarchical sub-clusterings trained by the features are integrated as a clustering ensemble based on the majority voting strategy, which is used to make optimal decisions on suspicious steganographers. Experimental results show that the proposed scheme is effective and efficient in identifying potential steganographers in large-scale social media networks, and has better performance when tested against the state-of-the-art steganographic methods.
- Subjects :
- Steganalysis
021110 strategic, defence & security studies
Steganography
Computer Networks and Communications
Computer science
business.industry
Feature extraction
Payload (computing)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0211 other engineering and technologies
Pattern recognition
Data_CODINGANDINFORMATIONTHEORY
02 engineering and technology
computer.file_format
JPEG
0202 electrical engineering, electronic engineering, information engineering
Discrete cosine transform
020201 artificial intelligence & image processing
Artificial intelligence
Safety, Risk, Reliability and Quality
Cluster analysis
business
computer
Transform coding
Subjects
Details
- ISSN :
- 15566021 and 15566013
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Information Forensics and Security
- Accession number :
- edsair.doi...........a447c7af3da683a3a660f17d4b170f41