International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
A Survey On Audio Watermarking Methods Nihala K. N, M.Tech student, Viswajyothi College of Engineering and Technology ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - As the use of digital multimedia data is
increasing day by day, the necessity of protecting the data from illegal copyright infringement, fingerprinting and other malicious attacks is also increasing. Watermarking is the process of embedding a piece of secret data to the host data to protect from these attacks. In audio watermarking a piece of data is embedded into the original host signal without affecting the signal quality. There are different techniques available for audio watermarking. In this paper audio watermarking along with its properties is explained and different watermarking techniques are also examined and the comparison result shows the advantages and disadvantages of each technique which implies watermarking based on Fibonacci numbers satisfies most of the watermarking properties. KeyWords: Audio watermarking, Watermark embedding and Watermark extraction. 1.INTRODUCTION Watermarking is the technique of hiding secret digital information in a carrier signal. A digital watermark is a piece of data to be embedded in digital media such as an audio, video or image data. It is used to identify the copyright ownership of the data or to protect it against illegal application. As digital audio files can be downloaded and copied easily they are more prone to copyright infringements. Audio watermarks are secret signal data used to prevent these problems. These signals are embedded in the original signal data and extracted by detection mechanism. One of the main factor in which audio watermarking scheme depends on is the imperfection of the human auditory system. There are several methods to embed the watermark data into the original audio signals. An efficient audio watermarking system must mainly satisfy four basic requirements. These are robustness, security, imperceptibility and payload. Some of the watermark properties are discussed below. Robustness : Digital signal may undergo different signal processing operations. Robustness is the property of the watermark data to withstand these signal attacks and to be extracted even after these attacks. Security : The watermark signal must not reveal about the embedded watermarked data. So the system will not be prone to unauthorized access. The security of the Š 2017, IRJET
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Impact Factor value: 5.181
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watermarking system lies in the security of the secret key used not on the embedding algorithm. Imperceptibility : Imperceptibility refers to the quality of the signal after watermark embedding. The quality of the signal must be retained even after embedding data for an effective watermarking method. Payload : Payload refers to the amount of secret data that can be added to the original signal without effecting the quality of the original signal. For audio signals, data payload refers to the number of watermark bits that can be reliably embedded within the original signal per unit of time. Payload is usually measured in bits per second (bps). Different audio watermarking techniques are being compared in this paper based on their embedding procedure. The rest of this paper is organized as follow: Section 2 describes various watermarking techniques and Section 3 contains the comparison between different watermarking techniques and finally we have the conclusion at Section 4.
2. LITERATURE SURVEY In [1] Mohamed F. Mansour and Ahmed H. Tewk proposes a new system for data embedding. The interval length between the audio signal’s salient points are changed. Then the embedding is done in the quantization indices of the intervals. The wavelet extrema of the signal envelope are used as the salient points. The algorithm is robust against different signal processing attacks. But watermark detection depends on the selection of threshold and is not robust against pitch shifting . The salient point lengths are modified for embedding the data. The salient point interval lengths are quantized after extracting and refining them in the extraction process. Wei Li, Xiangyang Xue, and Peizhong Lu proposes a novel content-dependent localized robust audio watermarking scheme , to combat synchronization attacks in [2]. First select the steady high-energy local regions that represent music edges by using different methods .The methods are either selects embedding regions on the original audio waveform directly, select the peaks on the audio envelope as reference to determine the embedding regions or selection based on music content analysis .After selecting the region then embed the watermark in these regions. Such regions will not be changed much for maintaining high auditory quality and in this way, the embedded watermark is robust against different distortions. The prime factor in watermark detection is accurately locating the embedding region . If the embedding region identification is inaccurate then the watermark detection ISO 9001:2008 Certified Journal
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