A novel image hash algorithm resistant to print-scan
Signal Processing
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A study on the randomness measure of image hashing
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Fragility analysis of adaptive quantization-based image hashing
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Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
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An extended image hashing concept: content-based fingerprinting using FJLT
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Lexicographical framework for image hashing with implementation based on DCT and NMF
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Structural Feature-Based Image Hashing and Similarity Metric for Tampering Detection
Fundamenta Informaticae
Random Gray code and its performance analysis for image hashing
Signal Processing
Robust image hash in Radon transform domain for authentication
Image Communication
Histogram-based image hashing for searching content-preserving copies
Transactions on data hiding and multimedia security VI
Video fingerprinting using Latent Dirichlet Allocation and facial images
Pattern Recognition
Robust 3D mesh model hashing based on feature object
Digital Signal Processing
Perceptual image hashing via wave atom transform
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Robust image hashing using non-uniform sampling in discrete Fourier domain
Digital Signal Processing
Perceptual image hashing with histogram of color vector angles
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Fast communication: Robust image hashing using ring-based entropies
Signal Processing
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Key-dependent 3D model hashing for authentication using heat kernel signature
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Memoryless hash-based perceptual image authentication
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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In this paper, we propose the use of non-negative matrix factorization (NMF) for image hashing. In particular, we view images as matrices and the goal of hashing as a randomized dimensionality reduction that retains the essence of the original image matrix while preventing intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as QR and singular value decomposition, produce low-rank bases which do not respect the structure (i.e., non-negativity for images) of the original data. We observe that NMFs have two very desirable properties for secure image hashing applications: 1) The additivity property resulting from the non-negativity constraints results in bases that capture local components of the image, thereby significantly reducing misclassification and 2) the effect of geometric attacks on images in the spatial domain manifests (approximately) as independent identically distributed noise on NMF vectors, allowing the design of detectors that are both computationally simple and, at the same time, optimal in the sense of minimizing error probabilities. Receiver operating characteristics analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for image hashing.