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Digital Image Watermarking

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  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. BackgroundWatermark 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 Watermark Original Image Watermarked 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 DCT IDCT 1000 Highest Coeff Water- marked Image Image Inter- leave Water- mark Conv Code • 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 • 512x512 “Mandrill” Image • See Handout • Both watermarks imperceptible • Alterations to original image • difficult to notice Watermarked Image with Coding

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

  10. Watermark Detection W2 Deinterleave, Viterbi Decode Correlation Coefficient Corrupted Image Extracted Watermark W1 Original Image 1000 Highest DCT Coeff Owner’s watermark • For no coding, deinterleave and decode block ignored • =E[W1*W2]/{ E[W12]E[W22]} • 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

  11. Convolutional Codes C0 Input=[...1011010101100000000] G0 = [1 1 1 1 0 1 0 1 1] G1 = [1 0 1 1 1 0 0 0 1] 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 State 0 … … … … 1 2 3 • 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. No Coding: Additive Noise(0,900) With 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 Unzign Convert 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