基于恒Q谱草图和GA-SVM的鲁棒音频复制-移动伪造检测

IF 7 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Zhaopin Su, Mengke Li, Guofu Zhang, Qinfang Wu, M. Li, Weiming Zhang, Xin Yao
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引用次数: 2

摘要

作为证据的录音在诉讼中变得越来越重要。在作为证据接受之前,通常需要音频法医专家帮助确定提交的录音是否被篡改或真实。在这一领域中,复制-移动伪造检测(CMFD)一直是盲音频取证中亟待解决的问题,其重点是发现来自同一音频记录的可能伪造物。然而,现有的方法大多采用理想化的预分割和人为的阈值选择来计算词段之间的相似度,这可能导致严重的误导和误判,特别是在高频词上。在这项工作中,我们提出了一种基于恒定Q谱草图(CQSS)和定制遗传算法(GA)和支持向量机(SVM)集成的检测和定位音频复制移动伪造的鲁棒方法。具体来说,CQSS特征首先通过对平方幅度常数Q变换的对数取平均值来提取。然后,利用自定义遗传算法结合支持向量机对CQSS特征集进行自动优化,同时获得最佳特征子集和分类模型。最后,将CQSS-GA-SVM集成方法分别与最先进的盲检测方法在真实世界的中文和英文语料库上进行了评估。实验结果表明,CQSS-GA-SVM对基于后处理的反取证攻击具有较强的鲁棒性,对重复片段长度、训练集大小、录音长度和伪造类型的变化具有较强的适应性,有助于提高音频取证专家的工作效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM
Audio recordings used as evidence have become increasingly important to litigation. Before their admissibility as evidence, an audio forensic expert is often required to help determine whether the submitted audio recordings are altered or authentic. Within this field, the copy-move forgery detection (CMFD), which focuses on finding possible forgeries that are derived from the same audio recording, has been an urgent problem in blind audio forensics. However, most of the existing methods require idealistic pre-segmentation and artificial threshold selection to calculate the similarity between segments, which may result in serious misleading and misjudgment especially on high frequency words. In this work, we present a robust method for detecting and locating an audio copy-move forgery on the basis of constant Q spectral sketches (CQSS) and the integration of a customised genetic algorithm (GA) and support vector machine (SVM). Specifically, the CQSS features are first extracted by averaging the logarithm of the squared-magnitude constant Q transform. Then, the CQSS feature set is automatically optimised by a customised GA combined with SVM to obtain the best feature subset and classification model at the same time. Finally, the integrated method, named CQSS-GA-SVM, is evaluated against the state-of-the-art approaches to blind detection of copy-move forgeries on real-world copy-move datasets with read English and Chinese corpus, respectively. The experimental results demonstrate that the proposed CQSS-GA-SVM exhibits significantly high robustness against post-processing based anti-forensics attacks and adaptability to the changes of the duplicated segment duration, the training set size, the recording length, and the forgery type, which may be beneficial to improving the work efficiency of audio forensic experts.
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来源期刊
IEEE Transactions on Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing 工程技术-计算机:软件工程
CiteScore
11.20
自引率
5.50%
发文量
354
审稿时长
9 months
期刊介绍: The "IEEE Transactions on Dependable and Secure Computing (TDSC)" is a prestigious journal that publishes high-quality, peer-reviewed research in the field of computer science, specifically targeting the development of dependable and secure computing systems and networks. This journal is dedicated to exploring the fundamental principles, methodologies, and mechanisms that enable the design, modeling, and evaluation of systems that meet the required levels of reliability, security, and performance. The scope of TDSC includes research on measurement, modeling, and simulation techniques that contribute to the understanding and improvement of system performance under various constraints. It also covers the foundations necessary for the joint evaluation, verification, and design of systems that balance performance, security, and dependability. By publishing archival research results, TDSC aims to provide a valuable resource for researchers, engineers, and practitioners working in the areas of cybersecurity, fault tolerance, and system reliability. The journal's focus on cutting-edge research ensures that it remains at the forefront of advancements in the field, promoting the development of technologies that are critical for the functioning of modern, complex systems.
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