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Multiple Re-Watermarking Scenarios

This presentation delves into the innovative field of multiple re-watermarking, highlighting various approaches and algorithms utilized in watermarking techniques. The focus is on experimental studies that analyze the effectiveness and properties of robust watermarking methods, emphasizing the fragility and robustness of algorithms in digital rights management and multimedia security. Key algorithms discussed include Wang's wavelet-based method, Corvi's approximation approach, and Koch's DCT-based algorithm. The findings suggest significant insights into watermarking's role in ownership verification and trading chains.

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Multiple Re-Watermarking Scenarios

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  1. Multiple Re-Watermarking Scenarios Severin Kampl, Daniel Mark

  2. „Multiple Re-Watermarking“ Team Carinthia Tech Institute, University of Applied Sciences, Austria Department of Computer Sciences, Salzburg University, Austria • Michael Dorfer Severin Kampl • Alexander Maier Daniel Mark • Andreas Palli Günter Scheer • Univ.-Prof. Mag. Dr. Andreas Uhl Multiple Re-Watermarking Scenarios

  3. Presentation Structure • Introduction • Multiple Re-Watermarking • Experimental Study • Conclusion and Perspectives Multiple Re-Watermarking Scenarios

  4. Introduction - Introduction- Multiple Re- Watermarking - Experimental Study - Settings & Methods- Results - Conclusion / Perspectives • DRM & multimedia security • Significantly different properties of Algorithms: • Fragility (integrity investigations) • Robustness (ownership claims) Multiple Re-Watermarking Scenarios

  5. Multiple Watermarking - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods- Results - Conclusion / Perspectives • Composite watermarking • One single embedding process • Segmented watermarking • Host data is partitioned • Successive watermarking (Re-Watermarking) • Embedding of one watermark after the other Our focus: Multiple Re-Watermarking with robust techniques Multiple Re-Watermarking Scenarios

  6. Target Scenario - Introduction - Multiple Re- Watermarking- Experimental Study - Settings & Methods- Results - Conclusion / Perspectives } owner info recipient info 1st sale re-sale re-sale . . . . . . host image embedding technique Scenario for reconstruction of the trading chain Multiple Re-Watermarking Scenarios

  7. Watermark Detection - Introduction- Multiple Re- Watermarking - Experimental Study - Settings & Methods- Results - Conclusion / Perspectives • Non-Blind Algorithm • Correct reference image is required • Blind Algorithm • No reference image is required Result: Correlation value of (e.g. B with B‘) Multiple Re-Watermarking Scenarios

  8. Experimental Study: Setting - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods- Results - Conclusion / Perspectives • „Lena“ Image (512 x 512 Px, 8 bpp) • Freely available watermarking toolbox • Algorithms: • Wang (non-blind, waveletbased, MF - HF) • Corvi (non-blind, waveletbased NF - MF) • Koch (blind, DCT-based, random blocks) • Final PSNR >= 38db Lena image, 512x512 Pixels, 8bpp Multiple Re-Watermarking Scenarios

  9. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives WANG - Algorithm Multiple Re-Watermarking Scenarios

  10. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives WANG - Algorithm Multiple Re-Watermarking Scenarios

  11. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives WANG - Algorithm Multiple Re-Watermarking Scenarios

  12. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives WANG - Algorithm Multiple Re-Watermarking Scenarios

  13. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives WANG - Algorithm Multiple Re-Watermarking Scenarios

  14. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives CORVI - Algorithm Multiple Re-Watermarking Scenarios

  15. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives CORVI - Algorithm Multiple Re-Watermarking Scenarios

  16. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives WANG  CORVI • Explanation: • Wang: most significant wavelet coefficients  always different coefficients • Corvi: all approximation subband coefficients  less overwriting Multiple Re-Watermarking Scenarios

  17. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives KOCH - Algorithm Multiple Re-Watermarking Scenarios

  18. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives KOCH - Algorithm Multiple Re-Watermarking Scenarios

  19. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives KOCH - Algorithm Multiple Re-Watermarking Scenarios

  20. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods - Results - Conclusion / Perspectives KOCH - Algorithm Multiple Re-Watermarking Scenarios

  21. - Introduction - Multiple Re- Watermarking - Experimental Study - Settings & Methods- Results - Conclusion & Perspectives Conclusion & Perspectives • WANG & KOCH as predicted • CORVI as predicted when using correct ref. Img. • Corvi also useable in „blind“ way • Large number of WMs detectable • Robustness concerning compression Multiple Re-Watermarking Scenarios

  22. Thank you for your Attentention! Michael Dorfer, Severin Kampl, Alexander Maier, Daniel Mark, Andreas Palli, Günter Scheer, Univ.-Prof. Mag. Dr. Andreas Uhl

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