Speech Steganalysis Based On The Delay Vector Variance Method

Autor: Emrah Yürüklü, Osman Hilmi Koçal, Erdoğan Dilaveroğlu
Přispěvatelé: Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü., Dilaveroğlu, Erdoğan
Rok vydání: 2016
Předmět:
Variance method
General Computer Science
Cover (telecommunications)
Delay vector variance
Speech recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Steganalysis
Convolutional Neural Network
Data Hiding
02 engineering and technology
Engineering
electrical & electronic
Surrogate data
Engineering
0202 electrical engineering
electronic engineering
information engineering
Speech
Steganography
Electrical and Electronic Engineering
Nonlinearity
Audio steganalysis
Computer science
artificial intelligence
Mathematics
Steganography
steganalysis
speech
chaos
false neighbors
Lyapunov exponent
surrogate data
delay vector variance
business.industry
SIGNAL (programming language)
020206 networking & telecommunications
Pattern recognition
Variance (accounting)
Computer science
False neighbors
Chaos
020201 artificial intelligence & image processing
Time-series
Artificial intelligence
Focus (optics)
business
Lyapunov exponent
Zdroj: Volume: 24, Issue: 5 4129-4141
Turkish Journal of Electrical Engineering and Computer Science
ISSN: 1300-0632
1303-6203
Popis: This study investigates the use of delay vector variance-based features for steganalysis of recorded speech. Because data hidden within a speech signal distort the properties of the original speech signal, we designed a new audio steganalyzer that utilizes delay vector variance (DVV) features based on surrogate data in order to detect the existence of hidden data. The proposed DVV features are evaluated individually and together with other chaotic-type features. The performance of the proposed steganalyzer method is also discussed with a focus on the effect of different hiding capacities. The results of the study show that using the proposed DVV features alone or in cooperation with other features helps in designing a distinctive audio steganalyzer, as cooperation with other chaotic-type features provides higher performances for stego and cover objects.
Databáze: OpenAIRE
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