Embedding Secrets in Digital Images and Their Compression Codes 嵌入機密訊息於數位影像及其壓縮碼之技術

1. Embedding Secrets in Digital Images and Their Compression Codes嵌入機密訊息於數位影像及其壓縮碼之技術 Advisor: Chin-Chen Chang1, 2 Student: Yi-Pei Hsieh2 1Dept. of Information Engineering and Computer Science, Feng Chia University 2Dept. of Computer Science and Information Engineering, National Chung Cheng University

2. Illegal user Motivation - data hiding (1/4) Information Public network Sender Receiver

3. Ciphertext Sender Plaintext Plaintext Receiver Motivation - data hiding (2/4) • Cryptography Encryption key Decryption key Meaningless & distorted Encryptionalgorithm Decryptionalgorithm Publicnetwork

4. Motivation - data hiding (3/4) Information Public network Illegal user Sender Information Receiver

5. Motivation - data hiding (4/4) Compressed codes: 1011101111….. 1101011001….. Information Publicnetwork Sender Receiver

6. Outline • Part I: embedding secrets into digital images • Image hiding • Part II: embedding secrets into compressed codes • Reversible hiding with high capacity in VQ domain

7. Part I: embedding secrets in digital images Image hiding: hiding multiple, relatively-large secret images into a relatively-small cover image

8. Stego image Cover image Goals Techniques ● Stego-image with high quality ●Compression method ● High hiding capacity • Vector quantization ●Modulus substitution ● Extracted secret image with Acceptable quality Image hiding Extracted secret image Secret image

9. X Euclidean distance Vector quantization (VQ) ■ How to generate a representative codebook ■ How to search the closest codeword  Two-codebook combination  Three-phase block matching

10. Cover image 0 1 . . . 0 1 . . . (43, 57, …, 40) (-5, 6, …, 10) M-2 M-1 N-2 N-1 Two codebook combination • The cover codebook • The difference codebook LBG clustering (k-means clustering) Secret image Difference image LBG clustering (k-means clustering)

11. Three-phase block matching 0 j k index j index k Secret image index j,1 index k,3 10 j 0 k 1 index k,1 index j,0 11 j 1 k 3

12. 00 11 11 +3 -1 1 2 3 0 1 2 3 Embedding The indices of chosen initial vectors (cover codebook) Modulus substitution The difference codebook LSBs of cover image q LSBs (q=2) The compressed indices 40:00101000 Hidden bits: 3(11) 39:00100111 Parameters

13. Experimental results (1/3) • Test images Baboon Tiffany Scene Lena Jet Pepper

14. Experimental results (2/3) • Embed a secret image into a cover image (i.e., Baboon) of the same size The PSNRs of the extracted secret images The PSNRs of the stego images Hu’s scheme The proposed scheme Wang-Tsai’s scheme

15. cover image The second secret image Hu’s scheme Experimental results (3/3) • Hide multiple images of large size into a small cover image The first secret image Wang-Tsai’s scheme The proposed scheme

16. Part II: embedding secrets in compressed codes Reversible data embedding with high embedding capacity

17. Reversible data hiding Public network Sender Original codes 1011101111….. Compressed codes: 1011101111….. 1101011001….. Information Receiver

18. Reversible data hiding • The similarity property of adjacent areas • Declustering • Put dissimilar codewords into the same cluster • Embedding • Cartesian product

19. Declustering decluster • G-1={C0,C7} • Declustering based on minimum spanning tree • Declustering based on short spanning path

20. Seed Block Seed Block Residual Block Side-match distortion If SMD(X, C4)< SMD(X,C1) andSMD(X, C4)< SMD(X, C6) Exchangeable Else Non-exchangeable Embedding procedure (1/2) Neighboring pixel intensities in an image are prettysimilar. Declustered result Seed indices X = (81, 15, 53, 34, 51,?, ?, ?, 91, ?, ?, ?, 49,?, ?, ?) Index table

21. Embedding procedure (2/2) Non-exchangeable Exchangeable Seed indices Index table Modified index table Declustered result (0010100)2 = (20)10 Secret bits: 1010010100 101

22. Experimental results (1/9) • 512×512 test images Baboon Barbara Boat Lena Pepper Tiffany

23. Experimental results (2/9) • Choose different codewords as the roots in the same minimum spanning tree Declustered result Declustered result

24. Experimental results (3/9) • Comparison of selecting different roots as the roots in the minimum-spanning-tree Baboon Barbara Boat ＊ Use the codebook with 1024 codewords＊Randomly choose codewords C58, C729, C134, C894, and C341 as roots

25. Experimental results (4/9) • Comparison of selecting different roots as the roots in the minimum-spanning-tree Lena Pepper Tiffany

26. Experimental results (5/9) • Declustering • The minimum-spanning-tree algorithm • The short-spanning-path algorithm

27. Experimental results (6/9) • Comparison between using the minimum-spanning-tree and the short-spanning-path algorithms Baboon Barbara Boat ＊Use the codebook with 1024 codewords

28. Experimental results (7/9) • Comparison between using the minimum-spanning-tree and the short-spanning-path algorithms Lena Pepper Tiffany

29. Experimental results (8/9) • Comparison of embedding capacities (bits) ＊Use the codebook with 512 codewords＊declustering using the short-spanning-path algorithm (101 groups)

30. Experimental results (9/9) • Comparison of time required for embedding process

31. Conclusions • To hide multiple, relatively-large secret images into a relatively-small cover image • We have proposed an image hiding method with two-codebook combination and three-phase block matching • To develop a reversible hiding with high capacity for VQ domain • We have proposed two declustering methods • We have applied the similarity property of adjacent areas in a natural image and Cartesian product

32. Future Research Directions (1/2) • Reversible data hiding • Restore the Original cover images after extracting the secret data • Need no extra data • Enhance the embedding capacity • Design the data hiding methods to other image formats • Binary and color images

33. Future Research Directions (2/2) • Develop the hiding schemes using other compressed codes • JPEG, JPEG2000, and BTC (block truncation coding) compressed codes

34. Thanks for your attention...