Robust Mesh-based Hashing for Copy Detection and Tracing of Images

Chun-Shien Lu, Chao-Yong Hsu, Shih-Wei Sun, and Pao-Chi Chang

Proc. IEEE Int. Conf. on Multimedia and Expo: special session on Media Identification, Taipei, Taiwan, 2004

Reporter: Jen-Bang Feng

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Outline

Watermarking and Hashing The Proposed Method Conclusions

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Watermarking and Hashing Digital Watermarking (Data Hiding)

Content has to be modified (a data hiding technique) Contents to be protected must be watermarked Measures “originality” Stand-along

Media Hashing (Fingerprinting) Content is not modified (a non-hiding technique) Can track the usage of contents already available in

the public domain Measure “similarity” Connection to database required

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Robust Signal Hashing Problem

Hash(Baboon)= XXX…

Hash(Lena)= YYY…

Hash(Lena 2)= ZZZ…

Should be very different

Should be sufficiently similar

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Perceptual Hashing The fragility of cryptography

hashing is too restricted Media data permits acceptable

distortions Media hashing needs

Robustness (error-resilience) Collision-free Fast searching (complexity) Scalability

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Track 1Track 1

Architecture for Robust Identification of Media Content

Track

Track 1Track 1Meta data

FingerprintGenerator

Database

CompareFingerprintGenerator

Test Track

If matchReturn Track ID Confidence

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The Proposed Method

DWTOriginalimage

Harrisdetector

Delaunaytesslation

Meshnormalization

Mesh-basedHash extraction

Lowest-frequencycomponent

Mesh generation

Normalizedmeshes

Hash sequence

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The Proposed Method

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Mesh Normalization

A

B

C

Mk

A’ B’

C’

Mknorm

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Mesh-Based Hashing

32

32

4x4 DCTTotal 64 blocks

otherwise.,0

ones,larger theis 1 if,1 sk

k

ACsH

64 bits per mesh, half 1’s and half 0’s

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Conclusions

The number of 0’s and 1’s are the same

Collision-free Robust against attacks

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Watermark AttacksWatermarking Attacks

RemovalAttack

GeometricalAttack

CryptographicAttack

ProtocolAttack

DenoisingLossy compressionQuantizationRemodulation Collusion Averaging

Global, local warpingGlobal, local transformsJittering

Brute force key searchOracle

Watermark inversionCopy attack

Voloshynovskiy et al. “attacks modeling: towards a second generation watermarking benchmark,” Signal Processing, 2001Kutter and Petitcolas, “A fair benchmark for image watermarking systems,” Proc. SPIE99

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Harris Detector

where I(x, y) is the grey level intensity and where A represents the integration of A on a given neighborhood. If at a certain point the two eigenvalues of the matrix are large, then a small motion in any direction will cause an important change of grey level. This indicates that the point is a corner.

2

2

,

yI

yI

xI

yI

xI

xI

M

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Harris Detector The corner response function is given

by:

where k is a parameter set to 0.04 (a suggestion of Harris). Corners are defined as local maxima of the cornerness function. Sub-pixel precision is achieved through a quadratic approximation of the neighborhood of the local maxima.

2det MtrkMR

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Delaunay Triangulation Voronoi Diagrams

每一點皆屬於最靠近的一區 http://infoshako.sk.tsukuba.ac.jp/~to

hyama/voro/edelacli.html