1. Spread Spectrum and Image Adaptive Watermarking A Compare/Contrast summary of: “Secure Spread Spectrum Watermarking for Multimedia” [Cox ‘97] and “Image-Adaptive Watermarking Using Visual Models” [Podilchuck ’98] Presented March 14, 2007 by Tony Boehm

2. © 2007 by Tony Boehm 2 Presentation Overview Relevant (high level) review of watermarks Briefly discuss the approach taken in both papers Compare the results of both papers Summarize the findings

3. © 2007 by Tony Boehm 3 Relevant Watermark Background and Terminology Perceptual invisibility Tamper resistance (Robustness) αResistance to attacks αCapacity of watermarking method

4. © 2007 by Tony Boehm 4 Secure Spread Spectrum Approach Begin by considering common processing operations Bold approach to place the watermark in the “most significant” spectral components Attacks on watermark require modifying coefficients that are most sensitive (perceptually) Watermark info is Gaussian distributed and added to a fixed number of “most significant”

5. © 2007 by Tony Boehm 5 Common Processing Operations

6. © 2007 by Tony Boehm 6 Watermarking steps Perform Fourier Transform/DCT Determine n most significant coefficients C(i) Generate Watermark = X - iid ~N(0,1) (length n) Add (weighted) X(i) to C(i) – there are many possibilities – Cox uses: Perform inverse Fourier Transform/DCT

7. © 2007 by Tony Boehm 7 Encoding Process

8. © 2007 by Tony Boehm 8 Recovering Watermark Perform Fourier Trans/DCT on both Watermarked image AND original image Subtract Watermarked image from original Run a detection algorithm on the difference X* and compare to the original watermark X. Cox uses a similarity metric: Where X* is the unknown watermark

9. © 2007 by Tony Boehm 9 Decoding Process

10. © 2007 by Tony Boehm 10 Why does it work? This method assumes we have access to the original image and know the watermark! Even with small SNR for each frequency band, signal detection techniques based on iid. assumptions can give a single value with high SNR. Proofs are given in the paper regarding probabilities of false positives – in general increasing n tends to cause larger similarity values if X and X* are genuinely related

11. © 2007 by Tony Boehm 11 Image – Adaptive Watermarking Using Visual Models Podilchuck extended Cox’s work in frequency domain and applied his suggestions for exploiting “perceptual models” and Just Noticeable Differences (JND)s Two adaptive methods were developed αIA – DCTDiscrete Cosine Transform αIA –W 9-7 bi-orthogonal wavelet basis Both methods adapt to the image based on HVS perception criteria

12. © 2007 by Tony Boehm 12 Image – Adaptive Approach The “power” of a watermark in other methods is generally conservative to allow for many images of varying texture. Some image compression techniques use an image dependant mask based on JNDs (perceptual coding) Extending this idea to watermarking: there is an upper bound on watermark intensities prior to “perceptual degradation”

13. © 2007 by Tony Boehm 13 Power of a watermark If we define a watermark sequence: where length is determined by the visual model and differs for every image I The power constraint of the watermark sequence is: where is the maximum power of an imperceptible watermark

14. © 2007 by Tony Boehm 14 Perceptual models for JNDs This paper does not provide the details of the models used but refers to papers that have developed image compression models based on HVS properties for: αFrequency Sensitivity αLuminance Sensitivity αContrast Masking See: A.B. Watson - “DCT Quantization matrices visually optimized for individual images” – SPIE Conf. Human Vision, Visual Processing and Digital Display VI - 1993

15. © 2007 by Tony Boehm 15 Frequency Sensitivity Describes HVS sensitivity to sine wave gratings at various frequencies Given an assumed, fixed, minimum viewing distance it is possible to calculate a static JND threshold for each frequency band. Frequency sensitivity depends only on viewing conditions and NOT on image content

16. © 2007 by Tony Boehm 16 Luminance Sensitivity Measures the effect of the detectable threshold of noise on a constant background. For the HVS this is a non-linear function that depends on local image characteristics

17. © 2007 by Tony Boehm 17 Contrast Masking Refers to the detect-ability of one signal in the presence of another. The effect is strongest when both signals are of the same frequency, orientation and location

18. © 2007 by Tony Boehm 18 General (IA) Watermarking Steps Perform frequency decomposition on original Calculate JNDs for the image Generate (iid) Watermark sequence: Create new frequency coefficients X*:

19. © 2007 by Tony Boehm 19 Or in 1000 words

20. © 2007 by Tony Boehm 20 Model Based Thresholds - Image independent, empirically determined Freq. Thresh. Lumin. Thresh. Contr. Thresh. -Image dependent, -Ratio of DC component at a given block/scale -a empirically determined -Max of Lumin. thresh or scaled version of freq. coeff. - w empirically determined

21. © 2007 by Tony Boehm 21 IA-DCT Sensitivity (Contrast) Mask

22. © 2007 by Tony Boehm 22 Image Adaptive - DCT Model (IA-DCT) Block based DCT is performed independently on (8X8) non-overlapping regions Uses “perceptually optimal”* quantization table for (global) frequency sensitivity, (locally) spatial luminance and contrast masking New Watermarked coefficients are: * A.B. Watson - “DCT Quantization matrices visually optimized for individual images” – SPIE Conf. Human Vision, Visual Processing and Digital Display VI - 1993

23. © 2007 by Tony Boehm 23 Image Adaptive Wavelet Model (IA – W) Hierarchical approach means different scales have different JND thresholds for each HVS model Has properties of both SS (global) and IA-DCT (local) watermarks New wavelet watermarked coefficients are: where l is the resolution level (1 – 4)and f is the frequency orientation 1,2 or 3

24. © 2007 by Tony Boehm 24 Detection Schemes Both use a normalized correlation detection scheme similar to the SS version Wavelet based method uses an average of the similarity scores calculated at each level - l and frequency orientation - f

25. © 2007 by Tony Boehm 25 Comparing the Approaches Recall the emphasis of these approaches: αRobust watermarks βDifficult or impossible to remove the watermark without degrading the image αPerceptual invisibility βAcross various textured images

26. © 2007 by Tony Boehm 26 Robustness Watermark is placed in “most significant” frequencies – cannot be removed without destroying the image (SS) Implied more data can be embedded in an image (more powerful) (IA-DCT and IA-W)

27. © 2007 by Tony Boehm 27 Watermark Capacity Image adaptive techniques can put more watermark into an image Image adaptive techniques have more watermark in the areas of most interest

28. © 2007 by Tony Boehm 28 Smooth (unstructured) Watermarks (IM1)

29. © 2007 by Tony Boehm 29 Smooth (unstructured) Watermarks (IM2)

30. © 2007 by Tony Boehm 30 Highly Structured Watermarks (IM3)

31. © 2007 by Tony Boehm 31 Highly Structured Watermarks (IM4)

32. © 2007 by Tony Boehm 32 (IM5)

33. © 2007 by Tony Boehm 33 (IM6)

34. © 2007 by Tony Boehm 34 (IM7)

35. © 2007 by Tony Boehm 35 (IM8)

36. © 2007 by Tony Boehm 36 Perceptual Invisibility Image adaptive techniques avoid putting too much watermark in smooth (low frequency) sections Since Spread Spectrum approach has a fixed size watermark the same α does not work for all images

37. © 2007 by Tony Boehm 37 Mixed Image (IM9)

38. © 2007 by Tony Boehm 38 (IM10)

39. © 2007 by Tony Boehm 39 Detecting Watermarks After Attack Print, Xerox Scan – discussed in [Cox] αA lot of high frequency noise is introduced into the image α Since water mark is in “most significant” spectral components (low frequency) it is retained Collusion – discussed in [Cox] αSpread Spectrum approach makes the watermark very robust

40. © 2007 by Tony Boehm 40 JPEG Compression

41. © 2007 by Tony Boehm 41 Crop then Compress

42. © 2007 by Tony Boehm 42 Scaling

43. © 2007 by Tony Boehm 43 Mis-Alignment Interpolation

44. © 2007 by Tony Boehm 44 Size of Compressed Images

45. © 2007 by Tony Boehm 45 Conclusions Since Podilchucks work was inspired by Cox the spectral approach and ideas of perceptual masking were a natural extension Cox’s SS approach of hiding the watermark in the “most significant” frequencies makes the watermarks robust to common attacks The adaptive techniques avoid putting too much watermark in the smooth portions of the image yet can accommodate much larger watermarks

46. © 2007 by Tony Boehm 46 Conclusions (continued) Image compression schemes based on HVS models for frequency, luminance and contrast sensitivity make IA techniques more flexible to a broader range of images Wavelet based watermarking has the advantages of both SS (global frequency) and DCT block based (spatially local) perceptual masking techniques