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Bayesian Structural Content Abstraction (BSCA) for Image Authentication

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Digital signature M ... BSCA extraction image Compute BSCA invariant moments ISS watermarking Protected image ISS watermarking Content feature computation ... – PowerPoint PPT presentation

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Title: Bayesian Structural Content Abstraction (BSCA) for Image Authentication


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

2
Outline
  • Contributions
  • Backgrounds
  • BSCA Scheme
  • BSCA Modeling
  • Spurious Region merging
  • Extending BSCA
  • Experimental Results
  • Conclusion Future Work

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

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
5
Backgrounds constraints
A good IA scheme
NCO/CO division is very application dependent
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

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.

8
Backgrounds a general IA scheme
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.

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.

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 images small-granularity
    content.

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.

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 DElias tree MRF
    segmentation algorithm.

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 humans 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.

15
BSCA Scheme modeling
  • The formulation can be further improved by
    introducing an explicit combination of NCOs.

16
BSCA Scheme modeling
The concrete configuration of clique we used.
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.

18
BSCA Scheme spurious region merging
  • Regions reordering by their sizes
  • Region size thresholding
  • Spurious regions merging according to local
    continuity and scale

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).

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.

21
Experimental Results
  • Experiments design

22
Experimental Results
NCOs COs
BSCA-Watermark 85 90
23
Experimental Results
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?

25
  • Thank you very much!
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