Information Hiding Techniques for Steganography and Digital Watermarking
Information Hiding Techniques for Steganography and Digital Watermarking
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Informed Watermarking
Next generation techniques for robust and imperceptible audio data hiding
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
IWDW'05 Proceedings of the 4th international conference on Digital Watermarking
Robust optimum detection of transform domain multiplicative watermarks
IEEE Transactions on Signal Processing
Rational dither modulation: a high-rate data-hiding method invariant to gain attacks
IEEE Transactions on Signal Processing - Part II
Double-Sided Watermark Embedding and Detection
IEEE Transactions on Information Forensics and Security - Part 1
IEEE Transactions on Information Theory
Watermark embedding: hiding a signal within a cover image
IEEE Communications Magazine
Some general methods for tampering with watermarks
IEEE Journal on Selected Areas in Communications
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
A new decoder for the optimum recovery of nonadditive watermarks
IEEE Transactions on Image Processing
Asymptotically optimal detection for additive watermarking in the DCT and DWT domains
IEEE Transactions on Image Processing
A novel high-capacity data-embedding system
IEEE Transactions on Image Processing
Analyzing the performance of dither modulation in presence of composite attacks
ICICS'11 Proceedings of the 13th international conference on Information and communications security
Reversible watermarking scheme for medical image based on differential evolution
Expert Systems with Applications: An International Journal
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In this paper, a novel arrangement for quantizer levels in the Quantization Index Modulation (QIM) method is proposed. Due to perceptual advantages of logarithmic quantization, and in order to solve the problems of a previous logarithmic quantizationbased method, we used the compression function of µ-Law standard for quantization. In this regard, the host signal is first transformed into the logarithmic domain using the µ-Law compression function. Then, the transformed data is quantized uniformly and the result is transformed back to the original domain using the inverse function. The scalar method is then extended to vector quantization. For this, the magnitude of each host vector is quantized on the surface of hyperspheres which follow logarithmic radii. Optimum parameter µ for both scalar and vector cases is calculated according to the host signal distribution. Moreover, inclusion of a secret key in the proposed method, similar to the dither modulation in QIM, is introduced. Performance of the proposed method in both cases is analyzed and the analytical derivations are verified through extensive simulations on artificial signals. The method is also simulated on real images and its performance is compared with previous scalar and vector quantization-based methods. Results show that this method features stronger a watermark in comparison with conventional QIM and, as a result, has better performance while it does not suffer from the drawbacks of a previously proposed logarithmic quantization algorithm.