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

2. Outline Performance Perturbed Motion Estimation Motivation Introduction

3. Video Steganography Adequate payloads Multiple applications Advanced technologies

4. Video Steganography  Conventional methods  Domain utilized --Intra frame --Spatial domain (pixels) --Transformed domain (DCT)  Disadvantages --Derived from image schemes --Vulnerable to certain existing steganalysis

5. Video Steganography  Joint Compression-Embedding  Using motion information  Adopting adaptive selection rules --Amplitude --Prediction errors

6. Motivation Arbitrary Modification Degradation in Steganographic Security Known/Week Selection rule

7. 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

8. Typical Inter-frame Coding 01011100… Entropy Coding DCT & QUANTIZATION Inter-MB Coding MB PARTITION

9. Regular Motion Estimation MBCOORDINATE R C MOTION VECTOR

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

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

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

13. Embedding Procedure Determine Applicable MBsWet Paper CodingPerturb Motion Estimation

14. Video Demo  Sequence:“WALK.cif”  Duration: 14 s  Message Embedded: 2.33KB  PSNR Degradation: 0.63dB

15. Experimental Date  20 CIF standard test sequence  352×288 ， 396 MBs  Embedding strength: 50 bit/frame

16. 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

17. Preliminary Security Evaluation Xuan’sPevny’s TNTPARTNTPAR 59.739.249.548.353.550.9

18. 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

19. Preliminary Security Evaluation Horizontal ComponentVertical Component TNTPARTNTPAR 91.510.851.253.546.950.2

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

21. Preliminary Security Evaluation Zhang’s TNTPAR 50.551.851.2

22. Summary Joint Compression-Embedding Using side information Improved security

23. Future works  Minimize embedding impacts  Different parity functions  Different selection rule designing criteria  Further Steganalysis  Larger and more diversified database