A Block Location Scrambling Algorithm of Digital Image Based on Arnold Transformation
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
Robust Watermarking Based on Norm Quantization Singular Value Decomposition and Zernike Moments
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
Chaos-based discrete fractional Sine transform domain audio watermarking scheme
Computers and Electrical Engineering
An Adaptive Audio Watermarking Method Based on Local Audio Feature and Support Vector Regression
SNPD '09 Proceedings of the 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing
Digital Image Watermarking in Integer Wavelet Domain Using Hybrid Technique
ACE '10 Proceedings of the 2010 International Conference on Advances in Computer Engineering
Blind and robust audio watermarking scheme based on SVD-DCT
Signal Processing
Localized audio watermarking technique robust against time-scale modification
IEEE Transactions on Multimedia
Histogram-Based Audio Watermarking Against Time-Scale Modification and Cropping Attacks
IEEE Transactions on Multimedia
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In this paper, combining the robustness of vector norm with that of the approximation components after the discrete wavelet transform (DWT), a blind and adaptive audio watermarking algorithm is proposed. In order to improve the robustness and imperceptibility, a binary image encrypted by Arnold transform as watermark is embedded in the vector norm of the segmented approximation components, the count of which depends on the size of the watermark image, after DWT of the original audio signal through quantization index modulation (QIM) with an adaptive quantization step selection scheme. Moreover, a detailed method has been designed to search the suitable quantization step parameters. Experimental results indicate that even though the capacity of the proposed algorithm is high, up to 102.4bps, this algorithm is still able to maintain good quality of the audio signal and tolerate a wide class of common attacks such as additive white Gaussian noise (AWGN), Gaussian Low-pass filter, Kaiser Low-pass filter, resampling, requantizing, cutting, MP3 compression and echo.