Robust Hash Functions for Digital Watermarking
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
Perceptual Similarity Metric Resilient to Rotation for Application in Robust Image Hashing
MUE '09 Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering
Robust video hashing based on radial projections of key frames
IEEE Transactions on Signal Processing - Part II
Unicity Distance of Robust Image Hashing
IEEE Transactions on Information Forensics and Security - Part 1
Robust and Secure Image Hashing via Non-Negative Matrix Factorizations
IEEE Transactions on Information Forensics and Security - Part 1
Robust and secure image hashing
IEEE Transactions on Information Forensics and Security
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Perceptual Image Hashing Via Feature Points: Performance Evaluation and Tradeoffs
IEEE Transactions on Image Processing
A robust image authentication method distinguishing JPEG compression from malicious manipulation
IEEE Transactions on Circuits and Systems for Video Technology
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Image hash is a content-based compact representation of an image for applications such as image copy detection, digital watermarking, and image authentication. This paper proposes a lexicographical-structured framework to generate image hashes. The system consists of two parts: dictionary construction and maintenance, and hash generation. The dictionary is a large collection of feature vectors called words, representing characteristics of various image blocks. It is composed of a number of sub-dictionaries, and each sub-dictionary contains many features, the number of which grows as the number of training images increase. The dictionary is used to provide basic building blocks, namely, the words, to form the hash. In the hash generation, blocks of the input image are represented by features associated to the sub-dictionaries. This is achieved by using a similarity metric to find the most similar feature among the selective features of each sub-dictionary. The corresponding features are combined to produce an intermediate hash. The final hash is obtained by encoding the intermediate hash. Under the proposed framework, we have implemented a hashing scheme using discrete cosine transform (DCT) and non-negative matrix factorization (NMF). Experimental results show that the proposed scheme is resistant to normal content-preserving manipulations, and has a very low collision probability.