用于数字音频干扰检测的判别成分分析增强型电网络频率特性融合

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Chunyan Zeng, Shuai Kong, Zhifeng Wang, Kun Li, Yuhao Zhao, Xiangkui Wan, Yunfan Chen
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引用次数: 0

摘要

随着电网络频率(ENF)分析技术的应用,数字音频篡改检测领域的研究取得了长足的进步,为预防犯罪和提高司法公正带来了显著的益处。然而,现有的方法,尤其是分析 ENF 相位和频率的方法,受到数据杂乱、冗余以及与标准分类算法不兼容等因素的阻碍,导致检测效率下降。本研究提出了一种采用判别成分分析(DCA)融合 ENF 特征的新方法,旨在直接解决这些问题。通过分析 ENF 相位和频谱的不同特征,我们的方法利用 DCA 有效地融合了这些特征。这种融合不仅增强了相位和频率特性之间的相关性,还通过有效降维简化了特征空间。此外,为了缩小与传统分类方法的差距,我们引入了级联深度随机森林算法,旨在对融合特征进行复杂的表征学习。这种级联处理大大提高了分类模型的精确度。在 Carioca 和新西班牙公共数据集上的实验结果表明,我们的方法在准确性和鲁棒性方面超越了当前最先进的方法,从而确立了其在数字音频篡改检测领域的优势。通过整合 DCA 算法来突出特征的独特性并最大化特征间的相关性,同时通过深度随机森林算法进行先进的表征学习,我们的方法显著提高了数字音频篡改检测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discriminative Component Analysis Enhanced Feature Fusion of Electrical Network Frequency for Digital Audio Tampering Detection

Discriminative Component Analysis Enhanced Feature Fusion of Electrical Network Frequency for Digital Audio Tampering Detection

Research in the domain of digital audio tampering detection has advanced significantly with the use of Electrical Network Frequency (ENF) analysis, presenting notable benefits for crime prevention and the enhancement of judicial integrity. However, the existing methodologies, particularly those analyzing ENF phase and frequency, are impeded by data clutter, redundancy, and incompatibilities with standard classification algorithms, leading to decreased detection efficacy. This study proposes a novel methodology employing Discriminant Component Analysis (DCA) for the fusion of ENF features, aiming to address these issues directly. By analyzing the distinct characteristics of ENF phase and frequency spectra, our approach uses DCA to merge these features effectively. This fusion not only amplifies the correlation between the features of phase and frequency but also simplifies the feature space through efficient dimensionality reduction. Additionally, to bridge the gap with traditional classification methods, we introduce a cascaded deep random forest algorithm, designed for intricate representational learning of the fused features. This sequential processing enhances the precision of our classification model significantly. Experimental results on both the Carioca and New Spanish public datasets demonstrate that our approach surpasses current state-of-the-art methods in terms of accuracy and robustness, establishing its superiority in the field of digital audio tampering detection. By integrating the DCA algorithm to accentuate feature uniqueness and maximize inter-feature correlation, alongside advanced representational learning via the deep random forest algorithm, our methodology markedly improves the accuracy of digital audio tampering detection.

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来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
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