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Video Steganography with Perturbed Motion Estimation

Explore how perturbed motion estimation improves video steganography security with adaptive selection rules, wet paper code, and motion embedding. Evaluate through preliminary security assessments.

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Video Steganography with Perturbed Motion Estimation

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

  2. Outline Introduction Motivation Perturbed Motion Estimation Performance

  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 Known/Week Selection rule Degradation in Steganographic Security Arbitrary Modification

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

  9. Regular Motion Estimation

  10. Perturbed Motion Estimation Cis 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

  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

  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

  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

  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

  24. Thank You !

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