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Multiple Re-Watermarking Scenarios

Explore multiple re-watermarking scenarios and experimental studies on robust algorithms for secure image protection and detection in digital content. Learn about composite watermarking and segmented watermarking methods.

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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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