In this paper, a robust and perceptually transparent single-level and multi-level blind audio watermarking scheme using wavelets is proposed. A randomly generated binary sequence is used as a watermark, and wavelet function coding is used to embed the watermark sequence in audio signals. Multi-level watermarking is used to enhance payload capacity and can be used for a different level of security. The robustness of the scheme is evaluated by applying different attacks such as filtering, sampling rate alteration, compression, noise addition, amplitude scaling, and cropping. The simulation results obtained show that the proposed watermarking scheme is resilient to various attacks except cropping. Perceptual transparency of watermark is measured by using Perceptual Evaluation of Audio Quality (PEAQ) basic model of ITU-R (PEAQ ITU-R BS.1387) on Speech Quality Assessing Material (SQAM) given by European Broadcasting Union (EBU). Average Objective Difference Grade (ODG) measured for this method is -0.067 and -0.080 for single-level and multi-level watermarked audio signals, respectively. In the proposed single-level digital audio watermarking scheme, the payload capacity is increased by 19.05% as compared to the single-level Chirp-Based Digital Audio Watermarking (CB-DAWM) scheme.

A robust and highly imperceptible audio watermarking technique is presented to secure the electronic patient record of Parkinson’s Disease (PD) affected patient. The proposed DCT-SVD based watermarking technique introduces minimal changes in speech such that the accuracy in classification of PD affected person’s speech and healthy person’s speech is retained. To achieve high imperceptibility the voiced part of the speech is considered for embedding the watermark. It is shown that the proposed watermarking technique is robust to common signal processing attacks. The practicability of the proposed technique is tested: by creating an android application to record & watermark the speech signal. The classification of PD affected speech is done using Support Vector Machine (SVM) classifier in cloud server.

This paper proposes a novel method for digital image watermarking, in which watermarks are embedded in the domain of fast para-metric transforms based on known spread spectrum approaches. Fast parametric transforms have the ability to adapt the forms of base vectors, which enables automatic selection of the domain of watermarking in relation to the pair: a marked image – a watermarking attack. The process of adapting the forms of fast parametric transforms is carried out with aid of the classical genetic algorithm with the fitting function based on the known measure of separability of watermarks. The effectiveness of the proposed method has been verified experimentally on the basis of the images of two classes, i.e. natural images and technical diagrams. The results taking into account both the efficiency of watermark embedding and the generated distortions in the marked images are summarized in tables and accompanied by an appropriate commentary.

In this paper, a new lifting wavelet domain audio watermarking algorithm based on the statistical characteristics of sub-band coefficients is proposed. First of all, an original audio signal was segmented and each segment was divided into two sections. Then, the Barker code was used for synchronization, the LWT (lifting wavelet transform) was performed on each section, a synchronization code and a watermark were embedded into the first section and the second section, respectively, by modifying the statistical average value of the sub-band coefficients. The embed strength was determined adaptively according to the auditory masking property. Experiments show that the embedded watermark has better robustness against common signal processing attacks than present algorithms based on LWT and can resist random cropping in particular.

Digital speech copyright protection and forgery identification are the prevalent issues in our advancing digital world. In speech forgery, voiced part of the speech signal is copied and pasted to a specific location which alters the meaning of the speech signal. Watermarking can be used to safe guard the copyrights of the owner. To detect copy-move forgeries a transform domain watermarking method is proposed. In the proposed method, watermarking is achieved through Discrete Cosine Transform (DCT) and Quantization Index Modulation (QIM) rule. Hash bits are also inserted in watermarked voice segments to detect Copy-Move Forgery (CMF) in speech signals. Proposed method is evaluated on two databases and achieved good imperceptibility. It exhibits robustness in detecting the watermark and forgeries against signal processing attacks such as resample, low-pass filtering, jittering, compression and cropping. The proposed work contributes for forensics analysis in speech signals. This proposed work also compared with the some of the state-of-art methods.