Yun CAO Xianfeng ZHAO Dengguo 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 methods  Domain utilized --Intra frame --Spatial domain (pixels) --Transformed domain (DCT)  Disadvantages --Derived from image schemes --Vulnerable to certain existing steganalysis

Video Steganography  Joint Compression-Embedding  Using motion information  Adopting 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 … Entropy Coding DCT & QUANTIZATION Inter-MB Coding MB PARTITION

Regular Motion Estimation MBCOORDINATE R C MOTION VECTOR

Perturbed Motion Estimation MBCOORDINATE R R’ C MOTION VECTOR C is applicable

Capacity  Number of applicable MBs  Free to choose criteria  SAD, MSE, Coding efficiency, etc

Wet Paper Code  Applicable MBs (Dry Spot)  Confine modification to them using wet paper code

Embedding Procedure Determine Applicable MBsWet Paper CodingPerturb 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 Steganalysis  A 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’sPevny’s TNTPARTNTPAR

Preliminary Security Evaluation  Motion vector map  Vertical and horizontal components as two images  A 39-d feature vector formed by statistical moments of wavelet characteristic functions (Xuan05)  SVM with the polynomial kernel

Preliminary Security Evaluation Horizontal ComponentVertical Component TNTPARTNTPAR

Preliminary Security Evaluation  Target Steganalysis  A 12-d feature vector derived from the changes in MV statistical characteristics (Zhang08)  SVM with the polynomial kernel

Preliminary Security Evaluation Zhang’s TNTPAR

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