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Robust digital image hashing algorithms for image identification

University of British Columbia

Image hashing has been a popular alternative of digital watermarking for copyright protection and content authentication of digital images, due to its two critical properties -- robustness and security. Also, its uniqueness and compactness make image hashing attractive for efficient image indexing and retrieval applications. In this thesis, novel image hashing algorithms are proposed to improve the robustness of digital image hashing against various perceptually insignificant manipulations and distortions on image content. Furthermore, image hashing concept is extended to the content-based fingerprinting concept, which combines various hashing schemes efficiently to achieve superior robustness and higher identification accuracy. The first contribution of the thesis is the novel FJLT image hashing, which applies a recently proposed low-distortion dimension reduction technique, referred as Fast Johnson-Lindenstrauss Transform (FJLT), into image hashing generation. FJLT shares the low distortion characteristics of random projections, but requires less computational cost, which are desirable properties to generate robust and secure image hashes. The Fourier-Mellin transform can also be incorporated into FJLT hashing to improve its performances under rotation attacks. Further, the content-based fingerprinting concept is proposed, which combines different FJLT-based hashes to achieve better overall robustness and identification capability. The second contribution of the thesis is the novel shape contexts based image hashing (SCH) using robust local feature points. The robust SIFT-Harris detector is proposed to select the most stable feature points under various content-preserving distortions, and compact and robust image hashes are generated by embedding the detected feature points into the shape contexts based descriptors. The proposed SCH approach yields better identification performances under geometric attacks and brightness changes, and provides comparable performances under classical distortions. The third contribution of this thesis addresses an important issue of compressing the real-valued image hashes into robust short binary image hashes. By exploring prior information from the virtual prior attacked hash space (VPAHS), the proposed semi-supervised spectral embedding approach could compress real-valued hashes into compact binary signatures, while the robustness against different attacks and distortions are preserved. Moreover, the proposed SSE framework could be easily generalized to combine different types of image hashes to generate a robust, fixed-length binary signature.

Text

2013-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/44384

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/