Robust and Secured Digital Audio Watermarking using Improved DWT-SVD-DFT Approach

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1112
Author(s):  
Krunalkumar N. Patel ◽  
Saurabh A. Shah ◽  
Dipti B. Shah

Today, the use of digital data like image, audio and video is tremendously increasing due to the advancement in technology and internet revolution. With this advancement to attain the one’s ownership and copyrights for this digital data is the biggest challenge. Digital watermarking is one of the technique to attain one’s ownership and copyrights with securely. It is the technique in which the owner’s copyright information can be embedded into the original media either in the form of an image, audio, text or video. There are two main factors we need to observe for this digital audio watermarking to maintain the robustness as well as imperceptibility against the piracy, malicious attacks, and various transformation operations. Though there are many challenges to achieve this results, in this paper, our proposed audio watermarking technique is used to improve the robustness, imperceptibility of the embedded information with security. For Security, in our proposed work we are using synchronized secret key concept with DSSS encryption algorithm and two important powerful transformation methods used that are DWT (Discreet Wavelet Transformation) up to 4-level to get lowest frequency sub-band and after that DFT is applied to get lowest frequency from sub-band found by DWT in which the modifications are done and then SVD (Singular value Decomposition)is applied to it, so that original audio file does not have any impact of watermark bits to get the better robustness and imperceptibility.  

Author(s):  
Aree Ali Mohammed

Transform-domain digital audio watermarking has a performance advantage over time-domain watermarking by virtue of the fact that frequency  transforms offer better exploitation of the human auditory system (HAS). In this research paper an adaptive audio watermarking is proposed based on the low and high wavelet frequencies band (LF, HF). The embedded watermark can be of any types of signal (text, audio and image). The insertion of the watermark data is performing in a frequency domain after applying discrete wavelet transformation on the cover audio segments. The normalize correlation and the signal to noise ratio metrics are used to test the performance of the proposed method in terms of the robustness and imperceptibility. Test results show that an improvement of the robustness against some type of attacks when the watermark is adaptively embedded in a different wavelet bands.


Author(s):  
Tribhuwan Kumar Tewari

Background & Objective: Revolution in digital multimedia is a boon to music industries, music creators for decades as digital music can be created, stored replicated and transferred easily and efficiently. But digitalization of multimedia becomes a curse when the multimedia content is illegally and freely distributed, shared across the network online or offline. Countering the illegal copying and distribution of digital media is the driving force behind the evolution of copyright protection and digital watermarking techniques. Methods: This paper presents the problem of piracy and the overview of the evolution of different digital audio watermarking techniques in time, transformed and compression domains to counter the problem of music piracy. The limitations of the audio watermarking techniques and the future scope for improvement are also presented. Results & Conclusion: This paper summarizes the evolution of audio watermarking techniques and reviews the existing watermarking techniques applied on audios. The limitations of the audio watermarking techniques and the future scope for improvement are also proposed. Additionally, the preliminaries for audio and brief of the properties which are exploited for watermarking of audio are presented.


2019 ◽  
Vol 6 (2) ◽  
pp. 141
Author(s):  
Togu Novriansyah Turnip ◽  
Jenny Doloksaribu ◽  
Vedtra Purba ◽  
Immanuel Saragih

<p class="Abstrak">Digital audio <em>watermarking</em> dibutuhkan untuk memberi perlindungan dari pembajakan musik secara ilegal dan pemberian hak cipta/kepemilikan. Penelitian ini menjelaskan perpaduan metode audio <em>watermarking</em> dimana informasi berupa hak cipta disisipkan ke dalam sinyal audio. Perpaduan dari metode DWT (<em>Discrete Wavelet Transfrom</em>) dan SVD (<em>Singular Value Decomposition</em>) digunakan untuk menyisipkan dan mengekstrak <em>watermark</em> dari sinyal audio. Informasi atau watermark tersebut dapat berupa gambar hitam putih (<em>biner</em>) atau huruf-huruf karakter ASCII. Pada penelitian ini sebuah gambar dijadikan sebagai <em>watermark</em> dengan berbagai variasi ukuran piksel seperti 10×10, 30×30, 40×40 dan 50×50 piksel. Hasil dari penyisipan <em>watermark</em> yang berukuran 30×30 piksel menghasilkan <em>imperceptibility</em> yang baik dengan nilai rata-rata diantara 43 sampai dengan 50 dB. Hasil eksperimen yang telah dilakukan juga menunjukkan bahwa kombinasi dari kedua metode tahan (<em>robustness</em>) terhadap beberapa serangan seperti <em>amplify</em>, <em>resampling</em> dan <em>invert</em>.</p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Judul2"><em>Digital audio watermarking is needed as a protection against online music piracy and </em><em>copyright issues</em><em>. This paper describes an audio watermarking</em><em> combination</em><em> method where </em><em>the </em><em>copyright information is imperceptibly added into the audio signal. The combination of discrete wavelet transform (DWT) and singular value decomposition (SVD) is used to embed and e</em><em>x</em><em>tract the watermark from the audio signal. The copyright information or watermark could be a binary logo or some unique binary pattern</em><em>s</em><em>. In this paper, a watermarked image </em><em>is </em><em>divided into four different capacities of dimension such as 10×10, 30×30</em><em>, </em><em>40×40 and 50×50 pi</em><em>x</em><em>el</em><em>s</em><em>.</em><em> </em><em>The result</em><em>s</em><em> of the watermarked image </em><em>are</em><em> imperceptibly added into the audio signal</em><em> and image with</em><em> 30×30 pixel </em><em>dimension </em><em>ha</em><em>s</em><em> the best mean result ranged from 43 to 50 dB</em><em>.</em><em> The experiment result also show</em><em>s</em><em> that the combination of DWT and SVD is robust against different attacks such as amplify, resampling and invert.</em></p><p class="Judul2"> </p>


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