On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Applied Cryptography
Handbook of Applied Cryptography
A Review of Audio Fingerprinting
Journal of VLSI Signal Processing Systems
Perceptual audio hashing functions
EURASIP Journal on Applied Signal Processing
Maximum-Likelihood Watermarking Detection on Fingerprint Images
BLISS '07 Proceedings of the 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security
Compressed Domain Perceptual Hashing for MELP Coded Speech
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Speech Hashing Algorithm Based on Short-Time Stability
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Perceptual image hashing based on virtual watermark detection
IEEE Transactions on Image Processing
Robust audio watermarking based on low-order zernike moments
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
On the automated recognition of seriously distorted musicalrecordings
IEEE Transactions on Signal Processing
Robust and Secure Image Hashing via Non-Negative Matrix Factorizations
IEEE Transactions on Information Forensics and Security - Part 1
Gaussian Mixture Modeling of Short-Time Fourier Transform Features for Audio Fingerprinting
IEEE Transactions on Information Forensics and Security
A new decoder for the optimum recovery of nonadditive watermarks
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Invariant image watermark using Zernike moments
IEEE Transactions on Circuits and Systems for Video Technology
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A new perceptual audio hashing algorithm based on maximum-likelihood watermarking detection is proposed in this paper. The idea is justified by the fact that the maximum-likelihood watermark detector responds similarly to perceptually close audio using a non-embedded watermark (i.e. virtual watermark). The feature vector, which is composed of the total amplitude of low-order Zernike moments of each audio frame, is modeled by the Gaussian or Rayleigh distribution. Then, the maximum-likelihood watermark detection is performed on the feature vector with the virtual watermarks generated by pseudo-random number generator to construct the hash vector. Extensive experiments over three large audio databases of different type (speech, instrumental music, and sung voice) demonstrate the efficiency of the proposed scheme in terms of discrimination, perceptual robustness and identification rate. It is also verified that the proposed scheme outperforms state-of-the-art techniques in perceptual robustness and can be applied in content-based search, successfully.