A hybrid Chebyshev-SVD based approach for robust audio watermarking application

IF 3.6 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Priyadharsini S., Aniruddha Kanhe
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引用次数: 0

Abstract

This paper presents a robust and imperceptible audio watermarking algorithm using Fast Chebyshev Transform (FCT) and Singular Value Decomposition (SVD) with energy based and perceptually guided frame selection. The method embeds binary watermark data into selected high-energy frames of audio signal, leveraging the energy and Zero Crossing Count (ZCC) to identify voiced regions in speech or high energy frames in music. The watermark bits are embedded into the singular values of FCT transformed frame matrices, ensuring minimal distortion to the host signal while maintaining resilience against signal processing attacks. The use of FCT enables efficient frequency-domain representation with reduced computational complexity compared to traditional transforms. Experimental results on speech and music signals demonstrate high transparency, measured by Signal-to-Noise Ratio (SNR) consistently above 61db and 0 Bit Error Rate(BER) before any attacks. This method achieves a payload capacity of 1200 bits/sec and robustness against noise addition, filtering, compression, resampling and various stirmark Benchmark attacks. Compared to existing methods, the proposed approach achieves lower distortion and improved robustness, making it suitable for copyright protection and secure audio authentication.
一种基于chebyhev - svd的混合音频水印方法
本文提出了一种基于能量和感知引导帧选择的快速切比雪夫变换(FCT)和奇异值分解(SVD)的鲁棒、不可感知音频水印算法。该方法将二进制水印数据嵌入到选定的高能音频信号帧中,利用能量和过零计数(ZCC)来识别语音中的浊音区域或音乐中的高能帧。水印位嵌入到FCT变换的帧矩阵的奇异值中,确保对主机信号的最小失真,同时保持对信号处理攻击的弹性。与传统变换相比,FCT的使用可以实现高效的频域表示,同时降低了计算复杂度。实验结果表明,语音和音乐信号具有很高的透明度,在任何攻击之前,信噪比(SNR)始终高于61db,误码率(BER)为0。该方法实现了1200比特/秒的有效载荷容量,并具有抗噪声添加、滤波、压缩、重采样和各种stirmark基准攻击的鲁棒性。与现有方法相比,该方法失真更小,鲁棒性更强,适用于版权保护和安全音频认证。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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