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Bayesian Structural Content Abstraction (BSCA) for Image Authentication

Bayesian Structural Content Abstraction (BSCA) for Image Authentication. Wei FENG ( w.feng@student.cityu.edu.hk ) Zhi-Qiang LIU School of Creative Media City University of Hong Kong Aug 27, 2004. Outline. Contributions Backgrounds BSCA Scheme BSCA Modeling Spurious Region merging

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Bayesian Structural Content Abstraction (BSCA) for Image Authentication

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  1. Bayesian Structural Content Abstraction (BSCA) for Image Authentication Wei FENG (w.feng@student.cityu.edu.hk) Zhi-Qiang LIU School of Creative Media City University of Hong Kong Aug 27, 2004 ICMLC04, Shanghai, China

  2. Outline • Contributions • Backgrounds • BSCA Scheme • BSCA Modeling • Spurious Region merging • Extending BSCA • Experimental Results • Conclusion & Future Work ICMLC04, Shanghai, China

  3. Contributions • A new model for parameterized image authentication which satisfies • robustness to NCOs • sensitivity to COs • the user-defined NCO/CO division • An extensible image authentication scheme ICMLC04, Shanghai, China

  4. Backgrounds • Image authentication (IA) is to protect the integrity of the content. An ideal IA approach should be able to tolerate “content preserving” operations (i.e., NCO, e.g. compression & rotation etc.) robustly, while detecting “content altering" (i.e., CO, e.g. object removing & replacement) modifications sensitively. A fundamental requirement of IA ICMLC04, Shanghai, China

  5. Backgrounds: constraints Robustness to NCOs Sensitivity to COs A good IA scheme NCO/CO division is very application dependent ICMLC04, Shanghai, China

  6. Backgrounds: former works • Fragile/semi-fragile watermarking • M. Wu & B. Liu, ICIP, 1998 • W. Zeng & B. Liu, IEEE Trans. IP, 1999 • Wong, ICIP 1998, IEEE Trans. IP, 2003 • … • Digital signature • M. Schneider & S.F. Chang, ICIP, 1996 • C.Y. Lin & S.F. Chang, 1998 • C.S. Lu & H.Y. Liao, IEEE Trans. MM, 2003 • … ICMLC04, Shanghai, China

  7. Backgrounds: our solution • Compared with DS, F & SF water-marking do not need an added secure channel. But the tamper detection capability is limited. • Both DS and watermarking are based on the selection of content represen-tation. ICMLC04, Shanghai, China

  8. Backgrounds: a general IA scheme ICMLC04, Shanghai, China

  9. BSCA Scheme: modeling • Generally, any feature extracted from the image (color, texture, edge, shape and structure etc.) can be viewed as a representation of its content. • The diverse features depict very different aspects of the image, thus have non-uniform representability to the content. If we use granularity to describe a feature's descriptive fineness as a content representation, we can see that color and texture are fine and local content representations, while structure is a global one. ICMLC04, Shanghai, China

  10. BSCA Scheme: modeling • Lu & Liao found that large granularity features, such as structure etc., are more reliable than small granularity features in IA. They designed an incidental distortion resistant scheme called SDS, based on the inter-scale relations of wavelet coefficients. Although they considered carefully the tradeoff between robustness and fragility, SDS still cannot perform robustly to a wide range of NCOs, such as low quality compression, global color adjustment and geometric distortions. • This is because that the structure in SDS was derived directly from pixels. • We define our content representation based on regions. ICMLC04, Shanghai, China

  11. BSCA Scheme: modeling • We model the image I as the sum of its underlying content C and an observation noise process N. We think the content should include at least two kinds of information: homogeneous region index map L that represents the structure, and principle color p(L) of each region that corresponds to the image’s small-granularity content. ICMLC04, Shanghai, China

  12. BSCA Scheme: modeling • Note that p(L) is a deterministic function which maps each homogeneous region to its dominant observation property univocally. • We can also assume that N is a Gaussian noise with zero-mean. • From (1) and (2), we can draw a statistical optimal estimation of the content within the Bayesian framework. ICMLC04, Shanghai, China

  13. BSCA Scheme: modeling • Furthermore, we can firstly get the optimal region map. • We model the region map L as a MRF with a second-order neighborhood system. Thus, the optimal L can be gotten by D’Elia’s tree MRF segmentation algorithm. ICMLC04, Shanghai, China

  14. BSCA Scheme: modeling • Actually, the estimation of an statistical optimal region map L is a Bayesian segmentation of the image. • This is rational because • The spatial distribution of homogeneous regions reflects human’s global perception of the image. • It will not change dramatically under a wide range of NCOs, but changes apparently under COs, such as object adding and removing etc. ICMLC04, Shanghai, China

  15. BSCA Scheme: modeling • The formulation can be further improved by introducing an explicit combination of NCOs. ICMLC04, Shanghai, China

  16. BSCA Scheme: modeling The concrete configuration of clique we used. ICMLC04, Shanghai, China

  17. BSCA Scheme: spurious region merging • We find many small fragments may exist in L. This is due to the existence of spurious regions in N and errors in the estimation to the noise process which should be removed. ICMLC04, Shanghai, China

  18. BSCA Scheme: spurious region merging • Regions reordering by their sizes • Region size thresholding • Spurious regions merging according to local continuity and scale ICMLC04, Shanghai, China

  19. BSCA Scheme: extending • Till now, a BSCA is a non-directed graph. • The graph can be naturally extended by integrating other favorable local features (regarding them as attributes of the associated vertex). ICMLC04, Shanghai, China

  20. Experimental Results • We already know that reference generation and transmission is the most important part in the IA. And, a desirable reference should be consistent with the content of the protected image. Thus, as an image content representation scheme, BSCA can be used to generate the standard reference feature. • Here, we only present a simple but efficient image authentication scheme which achieves both robustness to NCOs and sensitivity to COs. ICMLC04, Shanghai, China

  21. Experimental Results conclusion image BSCA extraction compare Compute BSCA invariant moments ISS watermarking Protected image ISS watermarking Content feature computation • Experiments design ICMLC04, Shanghai, China

  22. Experimental Results ICMLC04, Shanghai, China

  23. Experimental Results ICMLC04, Shanghai, China

  24. Conclusion & Future Work • We proposes a hierarchical image content representation scheme BSCA for IA. Because it is based on the fundamental criterion, it is robust against NCOs with little sacrifice of sensitivity to COs. • Moreover, as a general content representation scheme, BSCA can also be used in many other image processing and computer vision problems, such as compression and CBR etc. • We may explore the possibility of using BSCA to represent individual objects in a scene and their transitions in a video sequence. • How to construct a rotation invariant distance metric of BSCA? ICMLC04, Shanghai, China

  25. Thank you very much! ICMLC04, Shanghai, China

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