Authors:
Mengyu Qiao
1
;
Andrew H. Sung
1
;
Qingzhong Liu
2
and
Bernardete M. Ribeiro
3
Affiliations:
1
Institute for Complex Additive Systems Analysis and New Mexico Tech, United States
;
2
Sam Houston State University, United States
;
3
University of Coimbra, Portugal
Keyword(s):
Steganalysis, Steganography, MP3, Neural-fuzzy inference systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Steganography provides a stealthy communication channel for malicious users, which jeopardizes traditional cyber security infrastructure. Due to the good quality and the small storage usage, compressed audio has been widely employed by online audio sharing, audio streaming broadcast, and voice over IP, etc. Several audio steganographic systems have been developed and published on Internet. Traditional blind steganalysis methods detect the existence of information hiding, but neglect the size and the location of hidden data. In this paper, we present a scheme to locate the modified segments in compressed audio streams based on signal analysis in MDCT transform domain. We create reference signals by reversing and repeating quantification process, and compare the statistical differences between source signals and reference signals. Dynamic evolving neural-fuzzy inference systems are applied to predict the number of modified frames. Finally, the frames of audio streams are ranked accordi
ng to their modification density, and the top ranked frames are selected as candidate information-hiding locations.
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