A robust associative watermarking technique based on similarity diagrams

1. A robust associative watermarking technique based on similarity diagrams Source: Pattern Recognition, Vol. 40, No. 4, pp. 1355-1367, 2007 Authors: Jau-Ji Shen and Po-Wei Hsu

2. Background Concept of Digital Watermarking

3. Framework

4. Discrete Cosine Transform Just Noticeable Distortion Original Image Association Rules Embedded Image Sobel Operator Watermark Introduction

5. Discrete Cosine Transform (DCT) Spatial domain Frequency domain DC FDCT IDCT AC

6. Just Noticeable Distortion (JND) Frequency domain (DCT) JND

7. (x, y) Threshold Sobel Operator (1) (2) (3) (0) Mask 1 (4) (5) (6) (7) Mask 2

8. Watermark-random sequence (1/2) original image watermarking key random sequence={-1,1,-1,-1,1,…,1,-1,1} selected coefficients={9,13,-1,3,-1} modified coefficients={-9,39,-3,-3,1} FDCT IDCT

9. Watermark-random sequence (2/2) test image test key random sequence={-1,1,-1,-1,1,…,1,-1,1} selected coefficients={-9,39,-3,-3,1} FDCT

10. Association Rules (K-itemset)

11. Original Image Embedded Image Watermark Key Concept Discrete Cosine Transform Just Noticeable Distortion Association Rules [b1I(k), b2I(k), b3I(k), b4I(k)] [b1W(k), b2W(k), b3W(k), b4W(k)] Sobel Operator

12. … 1 2 3 8 … 9 k … 57 62 63 64 Position alignment items (0) (4) Block mean value (5) (1) (-1)*65+(-2)*80+(-1)*80 +0*80+0*75+0*75 +1*60+2*80+1*130=45 (2) (6) (3) (7) watermarking key watermarkW random sequence={1,1,-1,…,1,-1,-1…,-1,-1,1}

13. … 1 2 3 8 … 9 DCT transform k … 57 62 63 64 Value alignment item -9 13 9 AC={0, -1, 2, 77, 9, 13, -3,…, -9, 20, 3,…, 33, 5, 0} sort(abs(AC))={77,33,20,…,13,9,9,5,…,1,0,0} select firstCelements

14. (M1=8, M2=7) Quantization Quantization and sieve Sieve Quantization smooth block

15. 15 -10 12 Coupling and alignment Coupling pair Alignment

16. 0 77 0 0 0 0 0 0 108 77 -5 8 7 -7 -3 2 9 0 0 0 0 0 0 0 9 -8 -6 -5 0 1 0 0 9 0 0 0 0 0 0 0 -1 -6 2 6 -3 1 0 0 0 0 0 0 0 0 0 0 1 -1 5 -2 1 2 -1 0 0 0 0 0 0 0 0 0 1 2 2 1 0 -1 -1 0 0 0 0 0 0 0 0 0 0 2 0 0 -1 0 -1 -1 0 0 0 0 0 0 0 0 1 -1 1 -1 1 -1 0 -1 0 0 0 0 0 0 0 0 0 -1 0 -1 0 -1 0 1 pair 0 1 1 1 1 -1 -1 1 106 60 8 7 -6 2 3 1 9 6 -8 -2 1 -5 3 2 1 -1 1 1 1 -1 -1 1 2 -2 2 1 1 0 1 0 1 1 -1 -1 1 -1 -1 1 -1 0 1 0 2 -2 1 0 1 1 1 -1 1 -1 -1 1 JND 0 1 2 -1 1 1 0 1 1 -1 1 -1 1 1 -1 1 1 0 1 -1 -2 2 1 0 -1 -1 1 1 -1 1 1 -1 1 -1 1 1 -1 1 -1 1 0 0 0 0 1 0 0 -2 -1 1 1 -1 1 1 -1 1 0 0 0 0 0 1 1 -1 Detection scheme pair JND

17. Experiments (1/4) 256x256 image, 208x208 watermark PSNR=36.062 PSNR=37.222 PSNR=35.563

18. more sharpening brightness adjustment(+40) more blurring cutting (50%) Gaussion noise (σ2=20) JPEG compression (1%) Experiments (2/4) 200 test keys (100th key)

19. Experiments (3/4) False-negative errors: an embedding image while watermark not being detected False-positive errors: a non-watermarked image but extracted result is yes 200 test keys (100th key)

20. Experiments (4/4) Comparisons of the proposed method and Fotopoulos’s method.

21. Conclusions • A novel associative watermarking concept is proposed • Robust against different attacks • Original image is needed while detecting watermark

22. DCT version of JND formula is a constant whose value is 0.649, stands for the DC coefficient value of the kth block in the image, is the average value of the DC coefficients from all the image blocks, is the value of the element in position (i, j) of the JPEG quantization matrix, is a constant set to be 0.7, and finally is the coefficient value of the kth block of the image. A. B. Watson, “DCT quantization matrices visually optimized for individual images,” Proc. SPIE, Vol. 1913 (1993) 202-216.

23. Peak Signal-to-Noise Ratio - PSNR