1. Digital Image Watermarking Er-Hsien Fu EE381K-15280 Student Presentation

2. Overview Introduction Background Watermark Properties Embedding Detection The Project Introduction Embedding Detection Conclusions

3. Introduction Watermark--an invisible signature embedded inside an image to show authenticity or proof of ownership Discourage unauthorized copying and distribution of images over the internet Ensure a digital picture has not been altered Software can be used to search for a specific watermark

4. Background Watermark Properties Watermark should appear random, noise-like sequence Appear Undetectable Good Correlation Properties High correlation with signals similar to watermark Low correlation with other watermarks or random noise Common sequences A) Normal distribution B) m-sequences W=[1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0 0]

5. Project: Introduction Possible for watermark to be binary sequence Error-correction coding techniques Use convolutional codes Decode by Viterbi algorithm Compare with non-coding method See if it improves watermark detection More or less robust to attacks? Additive noise, JPEG Compression, Rescale, Unzign Performance assessed by correlation coefficient

6. Watermark Embedding WatermarkOriginal ImageWatermarked image Watermark placed into information content of Original Image to create Watermarked Image Image Content Spatial Domain (Least Significant Bit) FFT - Magnitude and Phase Wavelet Transforms DCT Coefficients

7. Setup-Watermark Embedding Image 1000 Highest Coeff Conv Code DCT Inter- leave Water- mark Water- marked Image IDCT DC Component Excluded for 1000 Highest Coefficients Interleaving prevents burst errors Watermarked Image Similar to original image Without coding, ignore Conv Code and Interleave block

8. Original Image Watermarked Image, No Coding Watermarked Image with Coding 512x512 “Mandrill” Image See Handout Both watermarks imperceptible Alterations to original image difficult to notice

9. Watermark Detection * =  Suspected Image Extracted Watermark Original Watermark Correlation Watermark Extracted from Suspected Image Compute correlation of Extracted and Original Watermark Threshold correlation to determine watermark existence

10. Watermark Detection Corrupted Image Original Image Extracted Watermark Owner’s watermark Correlation Coefficient 1000 Highest DCT Coeff Deinterleave, Viterbi Decode For no coding, deinterleave and decode block ignored  =E[W1*W2]/{ E[W1 2 ]E[W2 2 ]} If W1=W2 then  =1 if W1 and W2 are independent, then  =0 if E[W1]=0 Corruptions are additive noise, JPEG Compression Image scaling, and UnZign W2 W1

11. Convolutional Codes Input=[...1011010101100000000] G0 = [1 1 1 1 0 1 0 1 1] G1 = [1 0 1 1 1 0 0 0 1] C0 C1 Output C0 = conv(G0,Input); Output C1=conv(G1,Input) Convolutional code implemented using linear shift registers Adds redundancy for error-correction Encoding/Decoding well researched Good coding performance, very popular

12. Viterbi Decoding 0 1 2 3 State …………………… Find most likely path through trellis Begin and end at all zero state Upper arrows => input=0, Lower arrow =>input=1 Every possible input/output combination is compared with the received output Optimal Decoding Method

13. With Coding: Additive Noise (0,900) No Coding: Additive Noise(0,900) Zero mean additive noise, variance=100, 400, 900 Both methods had high correlation Coding method performed slightly better For variance = 900  (no coding) = 77% p (coding) = 84%

14. 4:1 JPEG Compression, No coding 4:1 JPEG Compression With Coding JPEG Compression: 1.4:1, 2.2:1, 4:1 ratio Both methods resistant to JPEG compression Coding method outperformed non-coding method Perfect detection for coding method

15. Watermark removal using UnzignConvert to grayscale and resize Unzign--watermark removal software Image resized to 512x512 and convert to grayscale before detection Moderate detection for without coding:  (no coding) = 57%  (coding) = 23% Coding method sensitive to resizing

16. Conclusions Convolutional coding more immune to additive noise and JPEG Compression Coding method fragile w.r.t. rescaled images Moderate detection levels for unzigned images Further Suggestion: Try block DCT Use Wavelet Transform Exploit Human Visual System

17. Questions