Key-dependent JPEG2000-based robust hashing for secure image authentication
EURASIP Journal on Information Security
Image-mapped data clustering: An efficient technique for clustering large data sets
Intelligent Data Analysis
Evaluation of JPEG2000 hashing for efficient authentication
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Fragility analysis of adaptive quantization-based image hashing
IEEE Transactions on Information Forensics and Security
The Martini Synch: joint fuzzy hashing via error correction
ESAS'07 Proceedings of the 4th European conference on Security and privacy in ad-hoc and sensor networks
Perceptual image hashing based on virtual watermark detection
IEEE Transactions on Image Processing
Cryptanalysis on an image scrambling encryption scheme based on pixel bit
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
An extended image hashing concept: content-based fingerprinting using FJLT
EURASIP Journal on Information Security
Random Gray code and its performance analysis for image hashing
Signal Processing
Robust image hash in Radon transform domain for authentication
Image Communication
Image forensic signature for content authenticity analysis
Journal of Visual Communication and Image Representation
Robust 3D mesh model hashing based on feature object
Digital Signal Processing
Key-dependent 3D model hashing for authentication using heat kernel signature
Digital Signal Processing
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A perceptual image hash function maps an image to a short binary string based on an image's appearance to the human eye. Perceptual image hashing is useful in image databases, watermarking, and authentication. In this paper, we decouple image hashing into feature extraction (intermediate hash) followed by data clustering (final hash). For any perceptually significant feature extractor, we propose a polynomial-time heuristic clustering algorithm that automatically determines the final hash length needed to satisfy a specified distortion. We prove that the decision version of our clustering problem is NP complete. Based on the proposed algorithm, we develop two variations to facilitate perceptual robustness versus fragility tradeoffs. We validate the perceptual significance of our hash by testing under Stirmark attacks. Finally, we develop randomized clustering algorithms for the purposes of secure image hashing.