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Optimized feature extraction for learning-based image steganalysis
Ying Wang,
Pierre Moulin
Electrical and Computer Engineering
Beckman Institute for Advanced Science and Technology
Statistics
Information Trust Institute
Coordinated Science Lab
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Dive into the research topics of 'Optimized feature extraction for learning-based image steganalysis'. Together they form a unique fingerprint.
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Keyphrases
Learning-based
100%
Probability Density Function
100%
Empirical Moment
100%
Optimized Feature Extraction
100%
Image Steganalysis
100%
Wavelet Transform
50%
Image Representation
50%
Learning Process
50%
Discrimination Power
50%
Classification Accuracy
50%
False Alarm Rate
50%
Image Statistics
50%
Detection Probability
50%
Supervised Learning
50%
Steganographic
50%
Hidden Message
50%
Informative Features
50%
Steganalysis Method
50%
Feature Dimension Reduction
50%
Impact Classification
50%
Photographic Images
50%
Three-level Optimization
50%
Stego Image
50%
Moments of Characteristic Function
50%
Engineering
Probability Density Function
100%
Feature Extraction
100%
Image Representation
50%
Dimensionality
50%
Classification Accuracy
50%
Characteristic Function
50%
Probability of Detection
50%
Probability of False Alarm
50%
Hidden Message
50%
Photographic Image
50%
Computer Science
Feature Extraction
100%
Steganalysis
100%
Probability Density Function
66%
Image Representation
33%
Wavelet Transforms
33%
Learning Process
33%
Classification Accuracy
33%
Dimensionality Reduction
33%
Detection Probability
33%
Characteristic Function
33%
Supervised Learning
33%
Photographic Image
33%