Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation
Dec 24, 2015
Yun CAOXianfeng ZHAODengguo FENG
Rennong SHENG
Video Steganography with Perturbed Motion Estimation
Outline
Performance
Perturbed Motion Estimation
Motivation
Introduction
Video Steganography
• Adequate payloads
• Multiple applications
• Advanced technologies
Video Steganography
Conventional methodsDomain utilized --Intra frame
--Spatial domain (pixels)
--Transformed domain (DCT)
Disadvantages --Derived from image schemes
--Vulnerable to certain existing steganalysis
Video Steganography
Joint Compression-EmbeddingUsing motion informationAdopting adaptive selection rules --Amplitude
--Prediction errors
Motivation
Arbitrary Modification
Degradation in Steganographic
Security
Known/Week Selection rule
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
Typical Inter-frame Coding
01011100…
Entropy Coding
DCT & QUANTIZATION
Inter-MB Coding
MB PARTITION
Regular Motion Estimation
MB COORDINATE
RC
12,8 4,4
MOTION VECTOR
8,4v
OthersCSimilarityRCSimilarity ,,
Perturbed Motion Estimation
MB COORDINATE
RR’C
12,8 14,7
MOTION VECTOR
8,4v
4,4
10,3'v
',, RCSimilarityRCSimilarity
1' vPvP
C is applicable
Capacity
Number of applicable MBsFree to choose criteriaSAD, MSE, Coding efficiency, etc
Wet Paper Code
Applicable MBs (Dry Spot)
Confine modification to them using wet paper code
Embedding Procedure
Determine Applicable MBs
Wet Paper Coding
Perturb Motion Estimation
Video Demo
Sequence:“WALK.cif”Duration: 14 sMessage Embedded: 2.33KBPSNR Degradation: 0.63dB
Experimental Date
20 CIF standard test sequence352×288 , 396 MBsEmbedding strength: 50
bit/frame
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
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
Preliminary Security Evaluation
Motion vector mapVertical and horizontal components as
two imagesA 39-d feature vector formed by
statistical moments of wavelet characteristic functions (Xuan05)
SVM with the polynomial kernel
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
True
Pos
itive
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 PositivesTr
ue P
ositi
ves
Preliminary Security Evaluation
Target SteganalysisA 12-d feature vector derived from the
changes in MV statistical characteristics (Zhang08)
SVM with the polynomial kernel
Preliminary Security Evaluation
Zhang’s
TN TP AR
50.5 51.8 51.2
Summary
• Joint Compression-Embedding
• Using side information
• Improved security
Future works
Minimize embedding impactsDifferent parity functionsDifferent selection rule designing criteria
Further SteganalysisLarger and more diversified database