Nima Khademi Kalantari

Robust Multiplicative Patchwork Method for Audio Watermarking

This paper presents a Multiplicative Patchwork Method (MPM) for audio watermarking. The watermark signal is embedded by selecting two subsets of the host signal features and modifying one subset multiplicatively regarding the watermark data, whereas another subset is left unchanged. The method is implemented in wavelet domain and approximation coefficients are used to embed data. In order to have an error-free detection, the watermark data is inserted only in the frames where the ratio of the energy of subsets is between two predefined values. Also in order to control the inaudibility of watermark insertion, we use an iterative algorithm to reach a desired quality for the watermarked audio signal. The quality of watermarked signal is evaluated in each iteration using Perceptual Evaluation of Audio Quality (PEAQ) method. The probability of error is also derived for the watermarking scheme and simulation results prove the validity of the analytical derivations. Simulation results show that MPM is robust against various common attacks such as noise addition, filtering, echo, MP3 compression, etc. In comparison to the original patchwork method and its modified versions, and some recent methods, MPM provides more robustness and inaudibility of the watermark insertion.

A Logarithmic Quantization Index Modulation for Perceptually Better Data Hiding

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 quantization-based method, we used the compression function of ¿ -Law standard for quantization. In this regard, the host signal is first transformed into the logarithmic domain using the ¿ -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 ¿ 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.

A Robust Image Watermarking in the Ridgelet Domain Using Universally Optimum Decoder

A robust image watermarking scheme in the ridgelet transform domain is proposed in this paper. Due to the use of the ridgelet domain, sparse representation of an image which deals with line singularities is obtained. In order to achieve more robustness and transparency, the watermark data is embedded in selected blocks of the host image by modifying the amplitude of the ridgelet coefficients which represent the most energetic direction. Since the probability distribution function of the ridgelet coefficients is not known, we propose a universally optimum decoder to perform the watermark extraction in a distribution-independent fashion. Decoder extracts the watermark data using the variance of the ridgelet coefficients of the most energetic direction in each block. Furthermore, since the decoder needs the noise variance to perform decoding, a robust noise estimation scheme is proposed. Moreover, the implementation of error correction codes on the proposed method is investigated. Analytical derivation of bit error probability is also carried out and experimental results prove its accuracy. Simulation also shows outstanding robustness of the proposed scheme against common attacks, especially additive white noise and JPEG compression.

Robust audio and speech watermarking using Gaussian and Laplacian modeling

In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented. At the receiver end, the optimal maximum likelihood (ML) detector aided by the archived information for Gaussian and Laplacian signals in noisy environment is designed and implemented. The performance of the proposed scheme is analytically calculated and verified by simulation. Then, we adapt the proposed scheme to speech and audio signals. To improve robustness, the algorithm is applied to low frequency components of the host signal. Besides, the power of the watermark is controlled elegantly to have inaudibility using perceptual evaluation of audio quality (PEAQ) and perceptual evaluation of speech quality (PESQ) algorithms. Experimental results over several audio and speech signals show the higher robustness of the proposed technique in comparison with other watermarking schemes presented so far.

A new approach for robust realtime Voice Activity Detection using spectral pattern

In this paper a Voice Activity Detection approach is proposed which applies a voting algorithm to decide on the existence of speech in audio signal. For this purpose, the proposed approach uses three different short time features along with the pattern of spectral peaks of every frame. Spectral peaks pattern is appropriate for determining vowel sounds in speech signal even in the presence of noise. Therefore this measure can be applicable in voice activity detection in which the vowels characterize the speech signal. Experiments show that incorporating this measure along with our recently proposed approach for VAD, will improve the results of the algorithm considerably while imposing little computational overhead. The proposed approach is evaluated on different datasets with various noises and SNR levels and satisfying results are achieved.

A Robust Audio Watermarking Scheme Using Mean Quantization in the Wavelet Transform Domain

In this paper, we present a mean quantization based audio watermarking scheme in the wavelet transform domain. The watermark data was embedded by quantizing the means of two selected bands of the wavelet transform of the original audio signal. One of the bands was in the lower frequency and the other one in the higher frequency ranges. Adaptive step sizes were used to achieve robustness and good transparency. As a result of selecting high and low frequency bands, this scheme is robust to both high- pass and low-pass attacks. The decoder detects the watermark data without any need to the original signal. The simulation results show that this watermarking scheme performs better than many recently proposed methods regarding robustness against common attacks such as MP3 compression, adding white Gaussian noise, filtering, resampling, etc.

Robust Multiplicative Audio and Speech Watermarking Using Statistical Modeling

In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented. At the receiver end, the optimal Maximum Likelihood (ML) detector aided by the channel side information for Gaussian and Laplacian signals in noisy environment is designed and implemented. The performance of the proposed scheme is analytically calculated and verified by simulation. Then, we adapt the proposed scheme to speech and audio signals. To improve robustness, the algorithm is applied to low frequency components of the host signal. Besides, the power of the watermark is controlled elegantly to have inaudibility using Perceptual Evaluation of Audio Quality (PEAQ) and Perceptual Evaluation of Speech Quality (PESQ) algorithms. Experimental results over several audio and speech signals show the higher robustness of the proposed technique in comparison with a recent watermarking scheme.

Logarithmic Quantization Index Modulation: A perceptually better way to embed data within a cover signal

In this paper, a new method for logarithmic Quantization Index Modulation (QIM) is proposed. In this regard a logarithmic function is first applied to the host signal. Then the transformed signal is quantized using uniform quantization as conventional QIM to embed watermark data within. Finally using inverse transform the watermarked signal is obtained. The watermark extraction is performed using minimum distance decoder. The optimum parameter for data embedding with minimum quantization distortion is derived. Also the probability of error is analytically calculated and verified by simulation. Furthermore data hiding using secret key is proposed and the probability of error is obtained. Simulation results show that the proposed method outperforms the conventional QIM in terms of robustness when the perceptual quality of watermarked image for both methods are similar. Moreover, simulation shows that the proposed scheme has outstanding robustness in comparison with a recent quantization based data hiding method.

A universally optimum decoder for multiplicative audio watermarking

In this paper, we propose a novel detector for multiplicative watermarking. The decoder extracts the watermark data by comparing the variances of the watermarked signal and the original signal. Due to the use of the variance test, the decoder works independent of the distribution of the host signal. This is the major advantage of this decoder per other decoders. The decoder is optimized under the Additive White Gaussian Noise channel by calculating the best threshold value. In order to show the optimal performance of this decoder on audio signals, a Maximum likelihood (ML) decoder is also introduced by considering a Gaussian distribution for the host signal. Furthermore, PEAQ algorithm is used for controlling the inaudibility of the watermark data insertion. Using this algorithm, the watermark strength factor updates automatically every 200 ms, according to the quality which is desired for the watermarked signal. Simulation results showed that the proposed decoder is extremely robust to the common audio watermarking attacks and slightly better than the ML decoder under Additive White Gaussian Noise attack.

Robust Multiplicative Patchwork Method for audio watermarking

A Multiplicative Patchwork Method (MPM) for audio watermarking is proposed in this paper. In order to embed watermark data within the host signal, two subsets of the host signal features are selected using two secret keys. Then, watermark data is inserted simply by multiplying one subset and leaving the other one unchanged. In order to have an error free detection in attack-free case, embedding is performed in the selected frames of the host signal which satisfies a certain condition. This method is implemented in the wavelet domain and approximation coefficients are used for data embedding. Furthermore, the inaudibility of watermark insertion is controlled using iterative approach aided by Perceptual Evaluation of Audio Quality (PEAQ) algorithm. Probability of error is derived and the validity of the analytical derivation is verified by simulation. Experimental results show that MPM is more robust than the previous patchwork methods against common attacks such as noise, filtering, MP3 compression, etc.