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

2 Outline Watermarking and Hashing The Proposed Method Conclusions

3 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

4 Robust Signal Hashing Problem Hash(Baboon) = XXX … Hash(Lena) = YYY … Hash(Lena 2) = ZZZ … Should be very different Should be sufficiently similar

5 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

6 Track 1 Architecture for Robust Identification of Media Content Track Track 1 Meta data Fingerprint Generator Database Compare Fingerprint Generator Test Track If match Return Track ID Confidence

7 The Proposed Method DWT Original image Harris detector Delaunay tesslation Mesh normalization Mesh-based Hash extraction Lowest-frequency component Mesh generation Normalized meshes Hash sequence

8 The Proposed Method

9 Mesh Normalization A B C MkMk A’A’ B’B’ C’C’ M k norm

10 Mesh-Based Hashing 32 4x4 DCT Total 64 blocks 64 bits per mesh, half 1 ’ s and half 0 ’ s

11 Conclusions The number of 0 ’ s and 1 ’ s are the same Collision-free Robust against attacks

12 Watermark Attacks Watermarking Attacks Removal Attack Geometrical Attack Cryptographic Attack Protocol Attack Denoising Lossy compression Quantization Remodulation Collusion Averaging Global, local warping Global, local transforms Jittering Brute force key search Oracle Watermark inversion Copy attack Voloshynovskiy et al. “ attacks modeling: towards a second generation watermarking benchmark, ” Signal Processing, 2001 Kutter and Petitcolas, “ A fair benchmark for image watermarking systems, ” Proc. SPIE99

13 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.

14 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.

15 Delaunay Triangulation Voronoi Diagrams 每一點皆屬於最靠近的一區 ama/voro/edelacli.html ama/voro/edelacli.html