基于混合特征和多通道GRU的伪造语音鉴别方法

doi:10.3969/j.issn.1671-1122.2021.10.001

信息网络安全 ›› 2021, Vol. 21 ›› Issue (10): 1-7.doi: 10.3969/j.issn.1671-1122.2021.10.001

基于混合特征和多通道GRU的伪造语音鉴别方法

潘孝勤, 杜彦辉()

中国人民公安大学信息网络安全学院,北京100038

收稿日期:2021-06-05 出版日期:2021-10-10 发布日期:2021-10-14
通讯作者: 杜彦辉 E-mail:duyanhui@ppsuc.edu.cn
作者简介:潘孝勤（1997—）,女,江苏,硕士研究生,主要研究方向为网络安全、人工智能|杜彦辉（1969—）,男,北京,教授,博士,主要研究方向为网络安全、大数据
基金资助:
国家重点研发计划(2017YFB0802804);中国人民公安大学基本科研业务费重大项目(2020JKF101)

Forged Voice Identification Method Based on Feature Fusion and Multi-channel GRU

PAN Xiaoqin, DU Yanhui()

College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China

Received:2021-06-05 Online:2021-10-10 Published:2021-10-14
Contact: DU Yanhui E-mail:duyanhui@ppsuc.edu.cn

摘要/Abstract

摘要：

为了解决现有鉴伪模型存在的泛化能力不强、检测准确率较低等难题,文章提出基于混合特征融合的多通道GRU伪造语音鉴别模型。该模型利用多通道挖掘不同输入特征的多尺度信息,同时引入注意力机制对多尺度特征进行融合并决策分类。在ASVspoof2019数据集上进行验证,所提方法对Logical Access伪造样本的检测准确率达到了96.30%,对Physical Access达到了87.33%,优于其他算法。实验结果证明,时频域特征融合的伪造语音检测方法能够学习更有效的真伪鉴别特征,获得更高的检测准确率。

关键词: 语音伪造检测, 多通道GRU, 特征融合, 深度学习

Abstract:

In order to solve the problems of poor generalization ability and low detection accuracy of existing counterfeit authentication models, this article proposes a three-channel GRU forged voice identification model based on hybrid feature fusion. Validated on the ASVspoof2019 dataset, the accuracy of the proposed method reaches 96.30% for the detection of fake Logical Access samples and 87.33% for that of the fake Physical Access samples, which is better than other algorithms. The experimental results prove that the fake voice detection method based on time-frequency domain feature fusion can learn more effective authenticity identification features and obtain higher detection accuracy.

Key words: speech forgery detection, multi-channel GRU, feature fusion, deep learning

中图分类号:

TP309

潘孝勤, 杜彦辉. 基于混合特征和多通道GRU的伪造语音鉴别方法[J]. 信息网络安全, 2021, 21(10): 1-7.

PAN Xiaoqin, DU Yanhui. Forged Voice Identification Method Based on Feature Fusion and Multi-channel GRU[J]. Netinfo Security, 2021, 21(10): 1-7.

图/表 8

图1

图2

图3

表1

表2

表3

表4

图4

参考文献 16

[1]	HANILI C, KINNUNEN T, SAHIDULLAH M, et al. Classifiers for Synthetic Speech Detection: A Comparison [C]//ISCA. Interspeech 2015, September 6-10, 2015, Dresden, Germany. Dresden: ISCA, 2015: 2057-2061.
[2]	SAHIDULLAH M, KINNUNEN T, HANILCI C. A Comparison of Features for Synthetic Speech Detection [C]//ISCA. Interspeech 2015, September 6-10, 2015, Dresden, Germany. Dresden: ISCA, 2015: 2087-2091.
[3]	CHEN Lianwu, GUO Wu, DAI Lirong. Speaker Verification Against Synthetic Speech [C]//IEEE. 7th International Symposium on Chinese Spoken Language Processing, November 29-December 3, 2010, Tainan, Taiwan, China. New Jersey: IEEE, 2010: 309-312.
[4]	YANG Jichen, HE Qianhua, HU Yongjian, et al. CBC-based Synthetic Speech Detection[J]. International Journal of Digital Crime and Forensics (IJDCF), 2019, 11(2):63-74.
[5]	TIAN Xiaohai, DU S, XIAO Xiong, et al. Detecting Synthetic Speech Using Long Term Magnitude and Phase Information [C]//IEEE. China Summit and International Conference on Signal and Information Processing (ChinaSIP), July 12-15, 2015, Chengdu, China. Chengdu: IEEE, 2015: 611-615.
[6]	WU Zhizheng, XIAO Xiong, CHNG E S, et al. Synthetic Speech Detection Using Temporal Modulation Feature [C]//IEEE. International Conference on Acoustics, Speech and Signal Processing, May 26-31, 2013, Vancouver, BC, Canada. Vancouver: IEEE, 2013: 7234-7238.
[7]	SAILOR H B, KAMBLE M R, PATIL H A. Unsupervised Representation Learning Using Convolutional Restricted Boltzmann Machine for Spoof Speech Detection [C]//ISCA. Interspeech 2017, August 20-24, 2017, Stockholm, Sweden. Stockholm: ISCA, 2017: 2601-2605.
[8]	TIAN Xiaohai, XIAO Xiong, CHNG E S, et al. Spoofing Speech Detection Using Temporal Convolutional Neural Network [C]//IEEE. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, December 13-16, 2016, Jeju, Korea(South). Jeju: IEEE, 2016: 1-6.
[9]	WANG Run, JUEFEI-XU F, HUANG Yihao, et al. DeepSonar: Towards Effective and Robust Detection of AI-synthesized Fake Voices [C]//ACM. 28th ACM International Conference on Multimedia, October 12-16, 2020, Seattle, WA, USA. New York: ACM, 2020: 1207-1216.
[10]	WU Zhenzong, DAS R K, YANG Jichen, et al. Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attacks [C]//ISCA. Interspeech 2020, October 25-19, 2020, Shanghai, China. Shanghai: ISCA, 2020: 1101-1105.
[11]	LI Xu, LI Na, WENG Chao, et al. Replay and Synthetic Speech Detection with Res2Net Architecture [C]//IEEE. International Conference on Acoustics, Speech and Signal Processing (ICASSP), June 6-11, 2021, Toronto, Ontario, Canada. Toronto: IEEE, 2021: 6354-6358.
[12]	GOMEZ-ALANIS A, PEINADO A M, GONZALEZ J A, et al. A Deep Identity Representation for Noise Robust Spoofing Detection [C]//ISCA. Interspeech 2018, September 2-6, 2018, Hyderabad, India. Hyderabad: ISCA, 2018: 676-680.
[13]	DUA M, JAIN C, KUMAR S. LSTM and CNN Based Ensemble Approach for Spoof Detection Task in Automatic Speaker Verification Systems[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(2):1-16. doi: 10.1007/s12652-020-02846-7 URL
[14]	GOMEZ-ALANIS A, PEINADO A M, GONZALEZ J A, et al. A Light Convolutional GRU-RNN Deep Feature Extractor for ASV Spoofing Detection [C]//ISCA. Interspeech 2019, September 15-19, 2019, Graz, Austria. Graz: ISCA, 2019: 1068-1072.
[15]	KAMBLE M R, PATIL H A. Detection of Replay Spoof Speech Using Teager Energy Feature Cues[EB/OL]. https://www.researchgate.net/publication/343659925_Detection_of_Replay_Spoof_Speech_Using_Teager_Energy_Feature_Cues, 2021-05-01.
[16]	VARIANI E, SAINATH T N, SHAFRAN I, et al. Complex Linear Projection (CLP): A Discriminative Approach to Joint Feature Extraction and Acoustic Modeling [C]//ISCA. Interspeech 2016, September 8-12, 2016, San Francisco, CA, USA. San Francisco: ISCA, 2016: 808-812.

编辑推荐 0

Metrics

阅读次数

全文

336

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	50	0	0	286

来源	本网站	其他网站

次数	336	0
比例	100%	0%

摘要

539

最新录用	在线预览	正式出版

0	0	539

	来源	本网站

	次数	539
	比例	100%

数据集分类	物理方式		逻辑方式
数据集分类	真实样本数	伪造样本数	真实样本数	伪造样本数
训练集	2500	2500	2500	2500
验证集	500	500	500	500
测试集	1500	1500	1500	1500

名称	信息
CPU	Intel(R) Core(TM) i9-10920X CPU @3.50 GHz
GPU	NVIDIA GeForce RTX 3090
内存	32 GB
操作系统	Ubuntu 16.04.6 LTS
Python版本	3.6.13
深度学习库	Tensorflow 2.1.0, keras 2.3.1

模型	ACC	EER	AUC	F1-score	Precision	Recall
RNN	88.87	11.98	84.64	89.84	83.64	97.04
LSTM	89.27	11.91	91.09	89.65	87.73	91.66
GRU	88.93	7.17	97.65	89.92	83.58	97.31
Multi-channel RNN	92.70	7.10	98.13	92.77	93.29	92.25
Multi-channel LSTM	94.27	5.82	93.20	94.37	94.00	94.74
Multi-channel GRU	96.30	0.34	99.99	96.48	93.20	100.00

模型	ACC	EER	AUC	F1-score	Precision	Recall
RNN	79.87	21.44	96.45	78.84	83.83	74.40
LSTM	82.43	19.35	92.99	81.52	86.78	76.85
GRU	81.70	18.41	94.11	80.98	85.02	77.31
Multi-channel RNN	83.77	15.26	88.75	82.13	92.25	74.01
Multi-channel LSTM	83.99	14.07	93.22	84.95	80.88	89.46
Multi-channel GRU	87.33	7.92	97.18	88.48	82.11	95.93

基于混合特征和多通道GRU的伪造语音鉴别方法

Forged Voice Identification Method Based on Feature Fusion and Multi-channel GRU

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 16

相关文章 15

编辑推荐 0

Metrics

本文评价

[1]	朱新同, 唐云祁, 耿鹏志. 基于特征融合的篡改与深度伪造图像检测算法[J]. 信息网络安全, 2021, 21(8): 70-81.
[2]	路宏琳, 王利明. 面向用户的支持用户掉线的联邦学习数据隐私保护方法[J]. 信息网络安全, 2021, 21(3): 64-71.
[3]	徐国天, 盛振威. 基于融合CNN与LSTM的DGA恶意域名检测方法[J]. 信息网络安全, 2021, 21(10): 41-47.
[4]	谭茹涵, 左黎明, 刘二根, 郭力. 基于图像特征融合的恶意代码检测[J]. 信息网络安全, 2021, 21(10): 90-95.
[5]	吴警, 芦天亮, 杜彦辉. 基于Char-RNN改进模型的恶意域名训练数据生成技术[J]. 信息网络安全, 2020, 20(9): 6-11.
[6]	王文华, 郝新, 刘焱, 王洋. AI系统的安全测评和防御加固方案[J]. 信息网络安全, 2020, 20(9): 87-91.
[7]	张蕾华, 黄进, 张涛, 王生玉. 视频侦查中人像智能分析应用及算法优化[J]. 信息网络安全, 2020, 20(5): 88-93.
[8]	王蓉, 马春光, 武朋. 基于联邦学习和卷积神经网络的入侵检测方法[J]. 信息网络安全, 2020, 20(4): 47-54.
[9]	毕新亮, 杨海滨, 杨晓元, 黄思远. 基于StarGAN的生成式图像隐写方案[J]. 信息网络安全, 2020, 20(12): 64-71.
[10]	程洋, 雷敏, 罗群. 基于深度学习的物联网终端设备接入认证方法[J]. 信息网络安全, 2020, 20(11): 67-74.
[11]	顾兆军, 郝锦涛, 周景贤. 基于改进双线性卷积神经网络的恶意网络流量分类算法[J]. 信息网络安全, 2020, 20(10): 67-74.
[12]	谢永恒, 冯宇波, 董清风, 王梅. 基于深度学习的数据接入方法研究[J]. 信息网络安全, 2019, 19(9): 36-40.
[13]	马春光, 郭瑶瑶, 武朋, 刘海波. 生成式对抗网络图像增强研究综述[J]. 信息网络安全, 2019, 19(5): 10-12.
[14]	方勇, 朱光夏天, 刘露平, 贾鹏. 基于深度学习的浏览器Fuzz样本生成技术研究[J]. 信息网络安全, 2019, 19(3): 26-33.
[15]	朱海麒, 姜峰. 人工智能时代面向运维数据的异常检测技术研究与分析[J]. 信息网络安全, 2019, 19(11): 24-35.