Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment

Author:

Huang Xinchao1,Liu Zihan1,Lu Wei1ORCID,Liu Hongmei1,Xiang Shijun2

Affiliation:

1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

2. College of Information Science and Technology, Jinan University, Guangzhou, China.

Abstract

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.

Publisher

IGI Global

Subject

Software

Reference21 articles.

1. Robust audio watermarking in the time domain

2. Exposing digital audio forgeries in time domain by using singularity analysis with wavelets

3. Detecting butt-spliced edits in forensic digital audio recordings.;A. J.Cooper;39th International Conference: Audio Forensics: Practices and Challenges,2010

4. Mp3 bit rate quality detection through frequency spectrum analysis

5. Farid, H. (1999). Detecting digital forgeries using bispectral analysis (MIT AI Memo AIM-1657). MIT.

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3