Video Steganography With Perturbed Motion Estimation

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



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 information

Adopting adaptive selection rules

--Amplitude--Prediction errors



Motivation

Arbitrary

Modification

Degradation in

SteganographicSecurity

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

R

C

12,8

4,4

MOTION VECTOR

8,4

v

OthersC Similarity RC Similarity ,,



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



Capacity

Number of applicable MBsFree to choose criteria

SAD, MSE, Coding efficiency, etc



Wet Paper Code

Applicable MBs

(Dry Spot)

Confinemodification to

them using wet

paper code



Embedding Procedure

Determine Applicable MBs

Wet Paper Coding

Perturb Motion Estimation



Video Demo

Sequence:“WALK.cif ” Duration: 14 s

Message Embedded: 2.33KB

PSNR Degradation: 0.63dB



Experimental Date

20 CIF standard test sequence

352×288， 396 MBs

Embedding 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




Xuan’s Pevny’s

TN TP AR TN TP AR

59.7 39.2 49.5 48.3 53.5 50.9




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




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




Target SteganalysisA 12-d feature vector derived from the

changes in MV statistical characteristics

(Zhang08)SVM with the polynomial kernel




Zhang’s

TN TP AR

50.5 51.8 51.2



Summary

• Joint Compression-Embedding

• Using side information

• Improved security



Future works

Minimize embedding impacts

Different parity functions

Different selection rule designing criteria

Further Steganalysis

Larger and more diversified database



Video Steganography With Perturbed Motion Estimation

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