Perceptual Watermarks for Digital Image and Video

1. Perceptual Watermarks for Digital Image and Video ECE 738 paper presentation Pei Qi ECE at UW-Madison pqi@cae.wisc.edu

2. What is ‘perceptual’ watermark • Prior knowledge • Perceptual watermark

3. Prior knowledge • Additive watermark • Ideal watermark Three principles - Transparency or imperceptibility - Robustness - Capacity Challenging problem - Conflicts - Tradeoff between transparency and robustness

4. Prior knowledge • Human visual system Three properties of the human visual system 1.Frequency sensitivity What’s freq. sensitivity Freq. sensitivity describes the human eye’s sensitivity to sine wave gratings at various freq. Given that the minimum viewing distance is fixed, it’s possible to determine a static just noticeable difference threshold for each freq. band. JND threshold The JND threshold is such that changes in the frequency content in the image in the particular frequency band below the threshold are not noticeable

5. Prior knowledge • Human visual system Three properties of the human visual system 2.Luminance sensitivity What’s luminance sensitivity Luminance sensitivity measures the effects of the detectability threshold of noise on a constant background, which is a nonlinear function and depends on local image characteristics. 3. Contrast masking Contrast masking allows more dynamic control of the JND threshold levels. Contrast masking refers to the detectability of one signal in the presence of another signal.

6. Prior knowledge • Summary What is our goal to introduce human visual system in watermarking application? 1. Determine if a watermark inserted into a image is invisible or not 2. We are always trying to insert the maximum strength and maximum length watermarks into an image, SINCE more watermarks are inserted - more robust to attacks - more likely to be detected Make use of properties of human visual system to adjust the watermark so that it’s perfect for both robustness and transparency JNDs JNDs generated from different properties provide the quantized thresholds for embedding watermarks. - upper bounds on watermark strength levels - upper bounds on watermark length (capacity) Note: JND thresholds are NOT a fixed value, which depend on different images and approaches

7. Perceptual watermark techniques • Image-Independent watermark • Image-dependent or Image-adaptive watermark

8. Image-Independent watermark • A typical method (Cox approach) • Key points • Place watermark in perceptually significant components (low frequency) (for robustness) • Modify by a small amount below Just-noticeable-difference (JND) • Use long random vector as watermark to avoid artifacts • Any difference if using other watermark instead (w-b images, logo)(for imperceptibility & robustness) • Embedding v’i = vi +  vi wi = vi (1+ wi) • Perform DCT on entire image and embed watermark in DCT coefficients • Choose N=1000 largest AC coeff. and scale {vi} by a random factor • Detection

9. seed random vector generator Original image marked image wmk N largest coeff. IDCT & normalize 2D DCT sort v’=v (1+ w) other coeff. Block diagram of Cox’s scheme

10. Implementation Avoiding to change the corresponding location of each coefficient in the image, when you sort the vector projected from matrix of DCT coefficients

11. Challenging problem • How to improve Cox approach • Global scaling factor is not suitable for all coefficients - Maybe beyond the threshold in some areas of image, especially obvious in the smooth background area • More explicitly compute Just-noticeable-difference • JND ~ max amount each frequency coefficient can be modified imperceptibly • Use i for each coefficients  finely tune watermark strength • Overhead - Cost of computation of thresholds for each coefficient

12. Image-dependent orImage-adaptive watermark • Block-based DCT approach • Wavelet DWT approach

13. Image-Adaptive watermark • General Image-Adaptive watermark scheme • X*u,v : The watermarked image • Xu,v : The original image • Wu,v : The sequence of watermark values • Ju,v : The computed JND for each coefficient • Question: Why Xu,v > Ju,v (from local image, considering properties of HVS)

14. Block-based DCT approach • Nonoverlapping 8x8 blocks • DCT applied to each block independently Xu,v,b: The DCT coefficients X*u,v,b: The watermarked DCT coefficients Wu,v,b: The sequence of watermark values tCu,v,b: The computed JND calculated from the visual model • Key points Block-by-block DCT How to derive tCu,v,b

15. Block-based DCT approach tFu,v : a frequency threshold value, which is an 8x8 matrix values for each DCT basis function tLu,v,b : Luminance sensitivity estimated by the formula. X0,0,b: DC coeff. for block b X0,0: DC coeff. Corresponding to the mean luminance of the display a: parameter controlling the degree of luminance sensitivity (empirical value=0.649) tCu,v,b :Contrast masking threshold, where w between 0 and 1, a empirical value for w is 0.7

16. Block diagram of IA-DCT approach Watermark sequence W(u,v) DCT Watermark Insertion Original image X(i,j) X(u,v) Watermarked image X*(u,v) J(u,v) Calculate JNDs

17. Wavelet DWT approach • Key point • Hierarchy Decomposition • The upper left corner: Lowest frequency band. • l: resolution level 1, 2, 3, 4 • F: frequency orientation 1, 2, 3 • Much simpler than DCT app. - Cost of computing JNDs

18. Wavelet DWT approach Xu,v,l,f : wavelet coefficient at position(u,v) in resolution level l and frequency orientation f X*u,v,l,f : watermarked wavelet coefficient Wu,v,l,f : watermark sequence tFl,f : computed frequency weight at level l and frequency orientation f, which could be further refined by adding image-dependent components like DCT approach

19. Detection • Detection scheme for Block-based DCT 1. Based on classical detection theory as SS detection (Cox) - Original image is subtract from watermarked image and correlation between the signal difference and the watermark sequence is determined - The correlation value is compared to a threshold to determine whether the received image contains the watermark.

20. Testing IA-DCT without original image Key points • Assume original image has been JPEG compressed • Feature vector {Xf}, XD is greater than ½ of its corresponding quantization table value Q • W is only inserted in {Xf} • A correlation measure c is found between {Zf} and W • A threshold test is performed on c to determine if the W under test is present in Z

21. Detection • Detection for Wavelet 1. First, the correlation is performed separately at each level 2. Second, We calculate the average for each resolution level l and freq. orientation f 3. At last, we choose the maximum correlation value over all the possible levels as well as freq. locations

22. Comparison • Image-Independent vs Image-Adaptive • Image Quality All acceptable, but SS watermark is most visible in the smooth background area. • Robustness to Compression and Cropping Winner: IA-W • Robustness to Scaling Winner: IA-W again • Robustness to shift Only IA-W survives

23. Video watermarks • Unique requirements for watermarks • Extension of the IA-DCT Technique to Video • Watermarking of MPEG-2 • Scene-Adaptive Video Watermarking • Watermarking Standards

24. Key points in paper • What’s the perceptual watermark • How does HVS work for watermark applications • Three typical watermarking techniques

25. Papers • Perceptual Watermarks for Digital Image and video RAYMOND B. WOLFGANG CHRISTINE I. PODILCHUK AND EDWARD J. DELP • Image-Adaptive Watermarking Using Visual Models CHRISTINE I. PODILCHUK AND WENJUN ZENG

26. Thank you for your attention • PPT file and papers can be downloaded from website http://www.cae.wisc.edu/~pqi/ece738/presentation/ • Contact info: Name: Qi, Pei Email: pqi@cae.wisc.edu