An Information Theoretic Image Steganalysis for LSB Steganography

Autor: Rajeev Kumar, Sonam Chhikara
Rok vydání: 2020
Předmět:
Information Systems and Management
Computer science
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0211 other engineering and technologies
Data_CODINGANDINFORMATIONTHEORY
02 engineering and technology
Management Science and Operations Research
Joint entropy
Grayscale
Theoretical Computer Science
Least significant bit
Histogram
Computer Science::Multimedia
0202 electrical engineering
electronic engineering
information engineering
Computer Science (miscellaneous)
Electrical and Electronic Engineering
Computer Science::Cryptography and Security
Steganalysis
021110 strategic
defence & security studies
Steganography
business.industry
020206 networking & telecommunications
Pattern recognition
Support vector machine
Computer Vision and Pattern Recognition
Artificial intelligence
business
Software
Zdroj: Acta Cybernetica. 24:593-612
ISSN: 2676-993X
0324-721X
Popis: Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio.
Databáze: OpenAIRE
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