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Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation
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Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Dec 24, 2015

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Page 1: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Yun CAOXianfeng ZHAODengguo FENG

Rennong SHENG

Video Steganography with Perturbed Motion Estimation

Page 2: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Outline

Performance

Perturbed Motion Estimation

Motivation

Introduction

Page 3: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Video Steganography

• Adequate payloads

• Multiple applications

• Advanced technologies

Page 4: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Video Steganography

Conventional methodsDomain utilized --Intra frame

--Spatial domain (pixels)

--Transformed domain (DCT)

Disadvantages --Derived from image schemes

--Vulnerable to certain existing steganalysis

Page 5: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Video Steganography

Joint Compression-EmbeddingUsing motion informationAdopting adaptive selection rules --Amplitude

--Prediction errors

Page 6: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Motivation

Arbitrary Modification

Degradation in Steganographic

Security

Known/Week Selection rule

Page 7: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

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

Page 8: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Typical Inter-frame Coding

01011100…

Entropy Coding

DCT & QUANTIZATION

Inter-MB Coding

MB PARTITION

Page 9: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Regular Motion Estimation

MB COORDINATE

RC

12,8 4,4

MOTION VECTOR

8,4v

OthersCSimilarityRCSimilarity ,,

Page 10: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

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

Page 11: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Capacity

Number of applicable MBsFree to choose criteriaSAD, MSE, Coding efficiency, etc

Page 12: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Wet Paper Code

Applicable MBs (Dry Spot)

Confine modification to them using wet paper code

Page 13: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Embedding Procedure

Determine Applicable MBs

Wet Paper Coding

Perturb Motion Estimation

Page 14: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Video Demo

Sequence:“WALK.cif”Duration: 14 sMessage Embedded: 2.33KBPSNR Degradation: 0.63dB

Page 15: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Experimental Date

20 CIF standard test sequence352×288 , 396 MBsEmbedding strength: 50

bit/frame

Page 16: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

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

Page 17: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

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

Page 18: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

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

Page 19: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

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

Page 20: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Preliminary Security Evaluation

Target SteganalysisA 12-d feature vector derived from the

changes in MV statistical characteristics (Zhang08)

SVM with the polynomial kernel

Page 21: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Preliminary Security Evaluation

Zhang’s

TN TP AR

50.5 51.8 51.2

Page 22: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Summary

• Joint Compression-Embedding

• Using side information

• Improved security

Page 23: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.

Future works

Minimize embedding impactsDifferent parity functionsDifferent selection rule designing criteria

Further SteganalysisLarger and more diversified database

Page 24: Yun CAO Xianfeng ZHAO Dengguo FENG Rennong SHENG Video Steganography with Perturbed Motion Estimation.