Blind SVD-based audio watermarking using entropy and log-polar transformation
01 Feb 2015-Vol. 20, pp 74-83
TL;DR: Simulation results indicate that the hidden watermark data is robust against different attacks and the proposed scheme has high data payload and provides superior performance compared to the state-of-the-art watermarking schemes reported recently.
Abstract: This paper proposes a blind singular value decomposition (SVD) based audio watermarking scheme using entropy and log-polar transformation (LPT) for copyright protection of audio signal. In our proposed scheme, initially the original audio is segmented into non-overlapping frames and discrete cosine transform (DCT) is applied to each frame. Low frequency DCT coefficients are divided into sub band and entropy of each sub band is calculated. Watermark data is embedded into the Cartesian components of the largest singular value obtained from the DCT sub band with highest entropy value of each frame by quantization. Simulation results indicate that the hidden watermark data is robust against different attacks. The comparison analysis shows that the proposed scheme has high data payload and provides superior performance compared to the state-of-the-art watermarking schemes reported recently.
Citations
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TL;DR: Experimental results under well-known practical attacks, such as compressions, resampling, and various types of signal processing, confirm that the proposed algorithm performs well compared to conventional audio watermarking algorithms.
Abstract: This paper proposes a blind digital audio water- marking algorithm that utilizes the quantization index modulation (QIM) and the singular value decomposition (SVD) of stereo audio signals. Conventional SVD-based blind audio watermarking algorithms lack physical interpretation since the matrix construction method for the input matrix for SVD is heuristically defined. However, in the proposed approach, because the SVD is directly applied to the stereo input signals, the resulting decomposed elements convey a conceptually meaningful inter- pretation of the original audio signal. As the proposed approach effectively utilizes the ratio of singular values, the embedded watermark is highly imperceptible and robust against volumetric scaling attacks; most QIM-based watermarking schemes are weak to these types of attacks. Experimental results under well-known practical attacks, such as compressions, resampling, and various types of signal processing, confirm that the proposed algorithm performs well compared to conventional audio watermarking algorithms.
62 citations
Cites methods or result from "Blind SVD-based audio watermarking ..."
...[25] suggested an SVD-based watermarking method that used entropy...
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...The imperceptibility and robustness results were compared with the results from the conventional methods proposed in [22], [23], [25]....
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TL;DR: This survey presents various aspects of watermarking, and various state-of-the-art of multimedia and database water marking is discussed, in the hope that researchers will be able to implement efficientWatermarking techniques for the security ofimedia and database.
Abstract: In today’s digital era, it is very easy to copy, manipulate and distribute multimedia data over an open channel. Copyright protection, content authentication, identity theft, and ownership identification have become challenging issues for content owners/distributors. Off late data hiding methods have gained prominence in areas such as medical/healthcare, e-voting systems, military, communication, remote education, media file archiving, insurance companies, etc. Digital watermarking is one of the burning research areas to address these issues. In this survey, we present various aspects of watermarking. In addition, various classification of watermarking is presented. Here various state-of-the-art of multimedia and database watermarking is discussed. With this survey, researchers will be able to implement efficient watermarking techniques for the security of multimedia and database.
54 citations
TL;DR: In this article, a comprehensive review about the various types of glaucoma, causes and possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning is presented.
Abstract: Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.
31 citations
TL;DR: A blind audio watermarking scheme in discrete cosine transform (DCT) domain based on singular value decomposition (SVD), exponential operation (EO), and logarithm operation (LO) which is highly robust against different attacks and has high data payload and shows low error probability rates.
Abstract: Digital watermarking has drawn extensive attention for copyright protection of multimedia data. This paper introduces a blind audio watermarking scheme in discrete cosine transform (DCT) domain based on singular value decomposition (SVD), exponential operation (EO), and logarithm operation (LO). In our proposed scheme, initially the original audio is segmented into non-overlapping frames and DCT is applied to each frame. Low frequency DCT coefficients are divided into sub-bands and power of each sub band is calculated. EO is performed on the sub-band with highest power of the DCT coefficients of each frame. SVD is applied to the exponential coefficients of each sub bands with highest power represented in matrix form. Watermark information is embedded into the largest singular value by using a quantization function. Simulation results indicate that the proposed watermarking scheme is highly robust against different attacks. In addition, it has high data payload and shows low error probability rates. Moreover, it provides good performance in terms of imperceptibility, robustness, and data payload compared with some recent state-of-the-art watermarking methods.
27 citations
Cites background or methods or result from "Blind SVD-based audio watermarking ..."
...Proposed DCT-SVD-EO-LO 172.39 0 (22.05 kHz) 0 (8 bits/sample) 6.42 (32 kbps) [21] DCT-SVD-LPT 172.39 1.56 (22.05 kHz) 0 (8 bits/sample) 8.67 (32 kbps) [8] Wavelet-based entropy 172.28~86.14 9.1 (22.05 kHz) -- 6.7 (128 kbps) [7] DWT-based energy proportion 114.82 6.92 (22.05 kHz) -- 5.71 (80 kbps) [12] EMD 46.9~50.3 3 (22.05 kHz) 0 (8 bits/sample) 1 (32 kbps) [15] DWT-SVD 45.90 2 (22.05 kHz) 0 (8 bits/sample) 1 (32 kbps) [16] SVD- DCT 43 0 (22.05 kHz) 0 (8 bits/sample) 3 (32 kbps) [17] Histogram 3 0 (--) 0 (8 bits/sample) 15 (128 kbps) [18] DWT-based Histogram 2 0 (16 kHz) 0 (8 bits/sample) 17.50 (64 kbps) [11] LWT -- 16.50 (36.750 kHz) 22.09 (8 bits/sample) 51.73 (128 kbps) Tab....
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...[21] introduced an audio watermarking method based on DCT, SVD, and Log-polar transform (LPT)....
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...Dhar et al. [21] introduced an audio watermarking method based on DCT, SVD, and Log-polar transform (LPT)....
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...1387 (International Telecommunication Union-Radio-communication Sector) standard [21] incorporates a psychoacoustic model....
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...Table 3 shows a comparison of SNR and MOS results between the proposed scheme and the several recent methods which are based on the reported results in the references [7–9], [12], [14–16], [19], [21]....
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01 May 2021
TL;DR: A robust watermarking technique using a chaotic kbest gravitational search algorithm (CKGSA) in DCT and SVD domain to eliminate the FPP and outperforms the other existing techniques in terms of imperceptibility and robustness.
Abstract: The existing SVD based watermarking techniques insert watermark into the singular component which causes the false positive problem (FPP). To overcome this problem, a robust watermarking technique using a chaotic kbest gravitational search algorithm (CKGSA) in DCT and SVD domain is proposed. To eliminate the FPP, the watermark is embedded into the principal component (PC) of the cover image rather than the singular value. The objective to use CKGSA is to optimize the value of multiple embedding factors so that robustness and imperceptibility can be balanced. Arnold transform (AT) is applied to the watermark image before embedding, to enhance security and robustness. The performance of the proposed technique has been experimented on a standard dataset and compared with the six recent state-of-the-art techniques including DS-DE, DS-PSO, DCT-SVD, DDS-PSO, DS-GSA, and DS-TLBO in terms of imperceptibility (PSNR, SSIM) and robustness (PSNR, NC). The experimental results demonstrate that the proposed technique outperforms the other existing techniques.
18 citations
References
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TL;DR: This work presents a watermarking procedure to embed copyright protection into digital audio by directly modifying the audio samples and introduces the notion of a dual watermark: one which uses the original signal during detection and one which does not.
506 citations
Journal Article•
TL;DR: In this article, a new measurement method is described that combines ideas from several earlier methods, and the method should meet the requirements of the user community, and it has been recommended by ITU Radiocommunication study groups.
Abstract: Perceptual coding of audio signals is increasingly used in the transmission and storage of high-quality digital audio, and there is a strong demand for an acceptable objective method to measure the quality of such signals. A new measurement method is described that combines ideas from several earlier methods. The method should meet the requirements of the user community, and it has been recommended by ITU Radiocommunication study groups.
408 citations
"Blind SVD-based audio watermarking ..." refers methods in this paper
...1387 (International Telecommunication Union-Radio-communication Sector) standard (Thiede et al., 2000)....
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...…conducted using the objective difference grade (ODG) which is one of the output values obtained from the perceptual evaluation of audio quality (PEAQ) measurement technique specified in ITU-R BS.1387 (International Telecommunication Union-Radio-communication Sector) standard (Thiede et al., 2000)....
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TL;DR: It is expected that copyright applications will be overshadowed by applications such as broadcast monitoring, authentication, and tracking content distributed within corporations, and a variety of applications emerging that add value to media, such as annotation and linking content to the Web.
Abstract: Electronic watermarking can be traced back as far as 1954. The last 10 years has seen considerable interest in digital watermarking, due, in large part, to concerns about illegal piracy of copyrighted content. In this paper, we consider the following questions: is the interest warranted? What are the commercial applications of the technology? What scientific progress has been made in the last 10 years? What are the most exciting areas for research? And where might the next 10 years take us? In our opinion, the interest in watermarking is appropriate. However, we expect that copyright applications will be overshadowed by applications such as broadcast monitoring, authentication, and tracking content distributed within corporations. We further see a variety of applications emerging that add value to media, such as annotation and linking content to the Web. These latter applications may turn out to be the most compelling. Considerable progress has been made toward enabling these applications--perceptual modelling, security threats and countermeasures, and the development of a bag of tricks for efficient implementations. Further progress is needed in methods for handling geometric and temporal distortions. We expect other exciting developments to arise from research in informed watermarking.
386 citations
"Blind SVD-based audio watermarking ..." refers background in this paper
...A comprehensive survey on watermarking can be found in Cox and Miller (2002) and Cvejic and Seppanen (2007)....
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TL;DR: Subjective and objective tests reveal that the proposed watermarking scheme maintains high audio quality and is simultaneously highly robust to pirate attacks, including MP3 compression, low-pass filtering, amplitude scaling, time scaling, digital-to-analog/analog- to-digital reacquisition, cropping, sampling rate change, and bit resolution transformation.
Abstract: This work proposes a method of embedding digital watermarks into audio signals in the time domain. The proposed algorithm exploits differential average-of-absolute-amplitude relations within each group of audio samples to represent one-bit information. The principle of low-frequency amplitude modification is employed to scale amplitudes in a group manner (unlike the sample-by-sample manner as used in pseudonoise or spread-spectrum techniques) in selected sections of samples so that the time-domain waveform envelope can be almost preserved. Besides, when the frequency-domain characteristics of the watermark signal are controlled by applying absolute hearing thresholds in the psychoacoustic model, the distortion associated with watermarking is hardly perceivable by human ears. The watermark can be blindly extracted without knowledge of the original signal. Subjective and objective tests reveal that the proposed watermarking scheme maintains high audio quality and is simultaneously highly robust to pirate attacks, including MP3 compression, low-pass filtering, amplitude scaling, time scaling, digital-to-analog/analog-to-digital reacquisition, cropping, sampling rate change, and bit resolution transformation. Security of embedded watermarks is enhanced by adopting unequal section lengths determined by a secret key.
235 citations
"Blind SVD-based audio watermarking ..." refers methods in this paper
...Most audio watermarking methods utilize either a time domain (Swanson et al., 1998; Lie and Chang, 2006) or a transform domain such as discretewavelet transform (DWT) (Chen et al., 2013, 2010a, 2010b), lifting wavelet transform (LWT) (Erçelebi and Batakçı, 2009), and fast Fourier transform (FFT)…...
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...Lie WN, Chang LC. Robust high quality time domain audio watermarking based on low frequency amplitude modification....
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...Most audio watermarking methods utilize either a time domain (Swanson et al., 1998; Lie and Chang, 2006) or a transform domain such as discretewavelet transform (DWT) (Chen et al., 2013, 2010a, 2010b), lifting wavelet transform (LWT) (Erçelebi and Batakçı, 2009), and fast Fourier transform (FFT) (Megı́as et al., 2010)....
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...Lie and Chang (Lie and Chang, 2006) introduced a method in which group a@sie.ics.saitama-u.ac.jp (T. Shimamura)....
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TL;DR: The proposed scheme has high payload and superior performance against MP3 compression compared to the earlier audio watermarking schemes, and shows low error probability rates.
204 citations