Watermarking Scheme with CS Encryption for Security and Piracy of Digital Audio Signals

2017 ◽  
Vol 8 (4) ◽  
pp. 38-60 ◽  
Author(s):  
Rohit Thanki ◽  
Komal Borisagar
Keyword(s):  
Compressive Sensing ◽  
Audio Signal ◽  
Curvelet Transform ◽  
Digital Audio ◽  
Watermark Image ◽  
Audio Signals ◽  
Speech Watermarking ◽  
Dct Coefficients ◽  
Watermarking Scheme ◽  
Better Than

In this article, a watermarking scheme using Curvelet Transform with a combination of compressive sensing (CS) theory is proposed for the protection of a digital audio signal. The curvelet coefficients of the host audio signal are modified according to compressive sensing (CS) measurements of the watermarked data. The CS measurements of watermark data is generated using CS theory processes and sparse coefficients (wavelet coefficients of DCT coefficients). The proposed scheme can be employed for both audio and speech watermarking. The gray scale watermark image is inserted into the host digital audio signal when the proposed scheme is used for audio watermarking. The speech signal is inserted into the host digital audio signal when the proposed scheme is employed for speech watermarking. The experimental results show that proposed scheme performs better than the existing watermarking schemes in terms of perceptual transparency.

Digital Audio Watermarking Based on QR Decomposition and Dither-Modulation Quantization

2015 ◽  
Vol 713-715 ◽  
pp. 1675-1681
Author(s):  
Lei Shi ◽  
Bai Long Yang ◽  
Peng Hui Wu
Keyword(s):  
Audio Signal ◽  
Qr Decomposition ◽  
Digital Audio ◽  
Test Results ◽  
Arnold Transform ◽  
Robust Test ◽  
Digital Audio Watermarking ◽  
Dither Modulation ◽  
Dct Coefficients ◽  
Good Transparency

An audio digital watermarking algorithm based on QR decomposition and dither-modulation quantization is proposed. Arnold transform is utilized to scramble watermarking image in order to improve the security of the algorithm. Then chaotic sequence encrypts the watermarking which was folded before. DCT coefficients of the original audio signal are selected to embed the encrypted watermarking after QR decomposition. Dither-modulation quantization changes the largest R component values of the QR decomposition. Imperceptibility, security and robust test results show that the present method is more robust, and possesses good transparency and high security compared with the scheme based on SVD and dither-modulation quantization.

scholarly journals An Effective Watermarking Method Based on Energy Averaging in Audio Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
S. E. Tsai ◽  
S. M. Yang
Keyword(s):  
Signal Processing ◽  
Data Compression ◽  
Error Correcting Code ◽  
Audio Signal ◽  
Segment Length ◽  
Signal Quality ◽  
Audio Signals ◽  
Segment Sequence ◽  
Audio Quality ◽  
Dct Coefficients

Methods based on discrete cosine transform (DCT) have been proposed for digital watermarking of audio signals; however, the watermark is often vulnerable to data compression and signal processing. This paper presents an effective audio watermarking method by energy averaging of DCT coefficients such that an audio signal with watermark is robust to data processing. The method is to divide an audio signal into segments by three parameters defining the segment length, the segment sequence of watermark location, and the frequency range of DCT coefficients for watermark location. An error correcting code is also integrated to improve audio signal quality after watermarking. Experimental results show that the method is robust to data compression and many other kinds of signal processing. No original signal is required for decoding the watermark. Comparison of watermarking performance with a recent work validates that the watermarking method has better audio quality and higher robustness.

An Improved Print-Scan Resistant Watermarking Scheme

2015 ◽  
Vol 731 ◽  
pp. 153-158
Author(s):  
Wei Guo ◽  
Wen Fa Qi
Keyword(s):  
Data Extraction ◽  
Visual Quality ◽  
Mapping Method ◽  
Local Complexity ◽  
Before And After ◽  
Dct Coefficients ◽  
The Mean ◽  
Image Pixels ◽  
Watermarking Scheme ◽  
Better Than

As an improvement of Cheng etal.’s print-and-scan (PS) digital watermarking scheme, the proposed method solves the data extraction failure problem caused by pixel displacement. We propose to embed the watermark into the middle band of DCT coefficients based on the fact that the mean of DCT coefficients (MDC) is zero before and after PS. Besides, a mapping method is designed which can distribute the change of average to the original image pixels according to the local complexity. Meanwhile, we use the JND model mentioned in Cheng et al.’s scheme to guarantee the visual quality. Extensive experiments show that the proposed scheme is effective to resist PS, and the performance of the proposed scheme is better than that of Cheng et al.’s scheme.

Audio Watermarking Scheme Using IMFs and HHT for Forensic Applications

2013 ◽  
Vol 5 (4) ◽  
pp. 55-67
Author(s):  
Saif alZahir ◽  
Md Wahedul Islam
Keyword(s):  
Time Scale ◽  
Audio Signal ◽  
Audio Watermarking ◽  
Intrinsic Mode Functions ◽  
Audio Signals ◽  
Mode Decomposition ◽  
Proof Of Ownership ◽  
Time Scale Modification ◽  
Audio Authentication ◽  
Watermarking Scheme

Audio signals and applications are numerous and ubiquitous. Most of these applications especially those on the Internet require authentication and proof(s) of ownership. There are several efficient methods in the literature address these crucial and critical concerns. In this paper, the authors present a new non-blind audio watermarking scheme for forensic audio authentication and proof of ownership. The proposed scheme is based on empirical mode decomposition and Hilbert Haung Transformation (HHT). In this method, the audio signal is decomposed into frames of 1024 sample each. These frames are further decomposed into its several mono-component signals called Intrinsic Mode Functions (IMF). These Intrinsic Mode Functions will serve as the addressee for the watermark. In this research, the chosen watermark is a pseudo random number generated by Matlab-7, which is added to the highest and lowest IMFs of each frame of the decomposed signal. This is done to accommodate for time scale modification attacks as well as MP3 compression respectively. Experimental results show that the watermarked audio signals maintained high fidelity of more than 20 dBs which meets the International Federation of Phonographic Industry requirements. The results also show that the proposed scheme is robust against signal processing attacks such as MP3, time scale modification, and resizing attacks.

scholarly journals A Multi-Level Robust and Perceptually Transparent Blind Audio Watermarking Scheme Using Wavelets

2015 ◽  
Vol 39 (4) ◽  
pp. 529-539 ◽  
Author(s):  
Farooq Husain ◽  
Omar Farooq ◽  
Ekram Khan
Keyword(s):  
Audio Watermarking ◽  
Digital Audio ◽  
Audio Signals ◽  
Blind Audio Watermarking ◽  
Single Level ◽  
Digital Audio Watermarking ◽  
Perceptual Evaluation ◽  
Payload Capacity ◽  
Multi Level ◽  
Watermarking Scheme

Abstract 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.

scholarly journals Design of Audio Embedding and De-embedding for 3G-SDI Based on FPGA

2018 ◽  
Vol 173 ◽  
pp. 03021
Author(s):  
Yaqing Liu ◽  
Lunhui Deng
Keyword(s):  
Theoretical Basis ◽  
Data Stream ◽  
Auxiliary Information ◽  
Audio Signal ◽  
Video Data ◽  
Digital Audio ◽  
Audio Signals ◽  
Audio Data ◽  
Video And Audio ◽  
The Cost

This design introduces the theoretical basis of digital audio embedding and de-embedding, and proposes a solution that Verilog language can be used to achieve 3G-SDI audio embedding and de-embedding. SDI video and audio data are input to the FPGA, and the audio signals can be embedded in the SDI line blanking after processing. Moreover, some auxiliary information is embedded in the SDI data, when you need these auxiliary information, you need to use the audio de-embedding process. The process of audio de-embedding is inversed with the process of embedding. It has been proved through practice that this scheme can effectively embed digital audio in SDI data stream, synchronize audio and video data, and can de-embed audio signal. The design is very versatile and can improve the efficiency of the design, thus effectively reducing the cost of the product.

scholarly journals Simultaneous Audio Encryption and Compression Using Compressive Sensing Techniques

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 863 ◽  
Author(s):  
Rodolfo Moreno-Alvarado ◽  
Eduardo Rivera-Jaramillo ◽  
Mariko Nakano ◽  
Hector Perez-Meana
Keyword(s):  
Compressive Sensing ◽  
Audio Signal ◽  
Communication Channels ◽  
Digital Information ◽  
Audio Signals ◽  
Sensing Matrix ◽  
Coding Schemes ◽  
Audio Encryption ◽  
Active Research ◽  
Audio Information

The development of coding schemes with the capacity to simultaneously encrypt and compress audio signals is a subject of active research because of the increasing necessity for transmitting sensitive audio information over insecure communication channels. Thus, several schemes have been developed; firstly, some of them compress the digital information and subsequently encrypt the resulting information. These schemas efficiently compress and encrypt the information. However, they may compromise the information as it can be accessed before encryption. To overcome this problem, a compressing sensing-based system to simultaneously compress and encrypt audio signals is proposed in which the audio signal is segmented in frames of 1024 samples and transformed into a sparse frame using the discrete cosine transform (DCT). Each frame is then multiplied by a different sensing matrix generated using the chaotic mixing scheme. This fact allows that the proposed scheme satisfies the extended Wyner secrecy (EWS) criterion. The evaluation results obtained using several genres of audio signals show that the proposed system allows to simultaneously compress and encrypt audio signals, satisfying the EWS criterion.

scholarly journals IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 676
Author(s):  
Andrej Zgank
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Audio Signal ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Animal Activity ◽  
The Impact ◽  
Acoustic Classification ◽  
Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

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