A Fourier transform based audio watermarking algorithm

Euschi, Salah; Khaldi, Amine; Kafi, Mohamed Redouane; Kahlessenane, Fares

doi:10.1016/j.apacoust.2020.107652

Cited by 54 publications

(9 citation statements)

References 29 publications

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“…It is concluded that the proposed methods achieve good results regarding capacity and security against the Fourier spectrum statistics based steganalysis proposed in [75]. Several variants of the Fourier transform, including discrete Fourier transform (DFT), fractional Fourier transform (FrFT), and quaternion discrete Fourier transform (QDFT), are used to process a cover audio signal [76].…”

Section: Transform Domainmentioning

confidence: 99%

LSB Data Hiding in Digital Media: A Survey

Tran¹,

Zepernick²,

Chu³

2022

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, offers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the different LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-offs between data hiding attributes.

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Section: Transform Domainmentioning

confidence: 99%

LSB Data Hiding in Digital Media: A Survey

Tran¹,

Zepernick²,

Chu³

2022

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

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“…When the signal and its fourier transform (FT) are replaced with discretized fourier transforms (DFT), a discrete Fourier transform is made. The Fourier transform function is as follows [30]:…”

Section: Feature Extraction 321 Fourier Transformmentioning

confidence: 99%

Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

Afsar¹,

Saqib²,

Ghadi³

et al. 2022

Computers, Materials &Amp; Continua

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Virtual reality is an emerging field in the whole world. The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities. Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games. The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room. To track the human movement, sensors Micro Processor Unit (MPU6050) are used that are connected with Bluetooth modules and Arduino responsible for sending the sensor data to the game. Further, the sensor data is sent to a machine learning model, which detects the game played by the user. The detected game will be operated on human gestures. A publicly available dataset named IM-Sporting Behaviors is initially used, which utilizes triaxial accelerometers attached to the subject's wrist, knee, and below neck regions to capture important aspects of human motion. The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity. The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%. The proposed system should apply to many systems such as construction, education, offices and the educational sector. Extensive experimentation proved the validity of the proposed system.

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“…It can accurately locate and even recover the tampered region of the audio, so it is mainly used for audio integrity protection [7,8]. Most robust watermarking algorithms are developed in frequency-domain, and the signal processing algorithms mainly include discrete Fourier transform (DFT) [9], discrete cosines transform (DCT) [10,11], and discrete wavelets transform (DWT) [12][13][14][15]. Megias et al [16] presented a blind audio watermarking algorithm in the DFT domain for overcoming the synchronization attack.…”

Section: Related Workmentioning

confidence: 99%

Audio Watermarking Algorithm with a Synchronization Mechanism Based on Spectrum Distribution

Ding

Wei³

2022

Security and Communication Networks

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In order to solve the problem that it is difficult to extract the watermark when the carried audio is subjected to malicious attacks, audio watermarking algorithm based on spectrum distribution is proposed. An eigenvalue is designed to represent the spectrum distribution of the specified frequency-band, and then the binary watermark is embedded by adjusting the difference of the eigenvalues between two adjacent frequency-bands. The polarity of embedding depth is determined by comparing the values of the eigenvalues, which will minimize the modification about the audio so as to improve transparency. The binary watermark can be extracted blindly by judging the difference of the eigenvalues. The improved synchronization mechanism takes the small frame with the largest energy in the voiced frame as the synchronization mark to search the embedding and extracting location of the watermark, which makes the algorithm have strong robustness. Experimental results show that the proposed algorithm has large payload capacity, good transparency, low complexity, and strong robustness against most attacks.

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A Fourier transform based audio watermarking algorithm

Cited by 54 publications

References 29 publications

LSB Data Hiding in Digital Media: A Survey

LSB Data Hiding in Digital Media: A Survey

Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

Audio Watermarking Algorithm with a Synchronization Mechanism Based on Spectrum Distribution

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