video steganography with perturbed motion estimation
TRANSCRIPT
8/13/2019 Video Steganography With Perturbed Motion Estimation
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Yun CAO
Xianfeng ZHAO
Dengguo FENG
Rennong SHENG
Video Steganography withPerturbed Motion Estimation
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Outline
Performance
Perturbed Motion Estimation
Motivation
Introduction
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Video Steganography
• Adequate payloads
• Multiple applications
• Advanced technologies
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Video Steganography
Conventional methodsDomain utilized
--Intra frame
--Spatial domain (pixels)--Transformed domain (DCT)
Disadvantages
--Derived from image schemes
--Vulnerable to certain existing steganalysis
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Video Steganography
Joint Compression-EmbeddingUsing motion information
Adopting adaptive selection rules
--Amplitude--Prediction errors
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Motivation
Arbitrary
Modification
Degradation in
SteganographicSecurity
Known/Week
Selection rule
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Motivation
How to improve?Using side information
--Information reduction process
--Only known to the encoder--Leveraging wet paper code
Mitigate the embedding effects
--Design pointed selection rules--Merge motion estimation & embedding
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Typical Inter-frame Coding
01011100…
Entropy Coding
DCT &
QUANTIZATION
Inter-MB Coding
MB PARTITION
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Regular Motion Estimation
MB COORDINATE
R
C
12,8
4,4
MOTION VECTOR
8,4
v
OthersC Similarity RC Similarity ,,
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Perturbed Motion Estimation
MB COORDINATE
R
R’
C
12,8
14,7
MOTION VECTOR
8,4v
4,4
10,3'v
',, RC Similarity RC Similarity
1' v P v P
C is applicable
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Capacity
Number of applicable MBsFree to choose criteria
SAD, MSE, Coding efficiency, etc
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Wet Paper Code
Applicable MBs
(Dry Spot)
Confinemodification to
them using wet
paper code
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Embedding Procedure
Determine Applicable MBs
Wet Paper Coding
Perturb Motion Estimation
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Video Demo
Sequence:“WALK.cif ” Duration: 14 s
Message Embedded: 2.33KB
PSNR Degradation: 0.63dB
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Experimental Date
20 CIF standard test sequence
352×288, 396 MBs
Embedding strength: 50 bit/frame
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Preliminary Security Evaluation
Traditional SteganalysisA 39-d feature vector formed by statistical
moments of wavelet characteristic
functions (Xuan05)A 686-d feature vector derived from the
second-order subtractive pixel adjacency
(Pevny10)SVM with the polynomial kernel
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Preliminary Security Evaluation
Xuan’s Pevny’s
TN TP AR TN TP AR
59.7 39.2 49.5 48.3 53.5 50.9
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Preliminary Security Evaluation
Motion vector mapVertical and horizontal components as
two images
A 39-d feature vector formed by statisticalmoments of wavelet characteristic
functions (Xuan05)
SVM with the polynomial kernel
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Preliminary Security Evaluation
Horizontal Component Vertical Component
TN TP AR TN TP AR
91.5 10.8 51.2 53.5 46.9 50.2
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False Positives
T r u e
P o s i t i v e s
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False Positives
T
r u e
P o s i t i v e s
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Preliminary Security Evaluation
Target SteganalysisA 12-d feature vector derived from the
changes in MV statistical characteristics
(Zhang08)SVM with the polynomial kernel
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Preliminary Security Evaluation
Zhang’s
TN TP AR
50.5 51.8 51.2
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Summary
• Joint Compression-Embedding
• Using side information
• Improved security
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Future works
Minimize embedding impacts
Different parity functions
Different selection rule designing criteria
Further Steganalysis
Larger and more diversified database