Audio recapture detection using deep learning

Luo, Da; Wu, Haojun; Huang, Jiwu

doi:10.1109/chinasip.2015.7230448

Cited by 12 publications

(3 citation statements)

References 17 publications

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“…In some other efforts, DNN framework is used [24,25,26,27,28]. For replay detection, algorithms were proposed using Electronic Network Frequency (ENF), MFCC and fundamental frequency, linear predictive residual signal, time envelope, stratified scattering decomposition coefficient and Inverse MFCC (IMFCC) respectively [29,30,31,32,33,34,35].…”

Section: Introductionmentioning

confidence: 99%

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Wang

Zhu

2020

Preprint

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Nowadays, speaker disguise is a common operation that presents a great challenge to social security. Therefore, it is important to recognize the authenticity of speech. Most of the current researches focus on speech spoofing, which simulates a target speaker to break through the state-of-art ASV systems by increasing false acceptance rate. Meanwhile, there is another type of disguise, i.e. de-identification, which transforms a speech signal without a target to increase the false rejection rate in order not to be recognized. It has received far less attention. Therefore, in this paper, we investigate the de-identification model and propose a method to detect de-identification speeches from genuine speeches by using a very deep dense convolutional network with 135 layers. The experimental results show that the average accuracy of the proposed method outperforms the reported state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Wang

Zhu

2020

Preprint

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“…Neural nets are also promising for detection of replay or presentation attacks [14,12]. The latest study by Muckenhirnet al [15] demonstrates the high accuracy of CNNs compared to systems based on handcrafted features for attack detection.…”

Section: Introductionmentioning

confidence: 99%

On the use of convolutional neural networks for speech presentation attack detection

Korshunov

Gonçalves

Violato

et al. 2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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Research in the area of automatic speaker verification (ASV) has advanced enough for the industry to start using ASV systems in practical applications. However, these systems are highly vulnerable to spoofing or presentation attacks (PAs), limiting their wide deployment. Several speechbased presentation attack detection (PAD) methods have been proposed recently but most of them are based on hand crafted frequency or phase-based features. Although convolutional neural networks (CNN) have already shown breakthrough results in face recognition, little is understood whether CNNs are as effective in detecting presentation attacks in speech. In this paper, to investigate the applicability of CNNs for PAD, we consider shallow and deep examples of CNN architectures implemented using Tensorflow and compare their performances with the state of the art MFCC with GMM-based system on two large databases with presentation attacks: publicly available voicePA and proprietary BioCPqD-PA. We study the impact of increasing the depth of CNNs on the performance, and note how they perform on unknown attacks, by using one database to train and another to evaluate. The results demonstrate that CNNs are able to learn a database significantly better (increasing depth also improves the performance), compared to hand crafted features. However, CNN-based PADs still lack the ability to generalize across databases and are unable to detect unknown attacks well.

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“…The benchmarking study on logical access attacks [15] finds GMMs to be more successful compared to two-class SVM (combined with an LBPbased feature extraction from [27]) in detecting synthetic spoofing attacks. Deep learning networks are also showing promising performance in simultaneous feature selection and classification [28].…”

Section: Classifiersmentioning

confidence: 99%

Presentation attack detection in voice biometrics

Korshunov¹,

Marcel²

2017

User-Centric Privacy and Security in Biometrics

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Recent years have shown an increase in both the accuracy of biometric systems and their practical use. The application of biometrics is becoming widespread with fingerprint sensors in smartphones, automatic face recognition in social networks and video-based applications, and speaker recognition in phone banking and other phone-based services. The popularization of the biometric systems, however, exposed their major flaw -high vulnerability to spoofing attacks [1]. A fingerprint sensor can be easily tricked with a simple glue-made mold, a face recognition system can be accessed using a printed photo, and a speaker recognition system can be spoofed with a replay of pre-recorded voice. The ease with which a biometric system can be spoofed demonstrates the importance of developing efficient anti-spoofing systems that can detect both known (conceivable now) and unknown (possible in the future) spoofing attacks.Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing biometric systems for practical and attack-resistant solutions. To be practical, however, an attack detection should have (i) high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient.One reason for the increasing demand for effective presentation attack detection (PAD) systems is the ease of access to people's biometric data. So often, a potential attacker can almost effortlessly obtain necessary biometric samples from social networks, including facial images, audio and video recordings, and even extract fingerprints from high resolution images. Therefore, various privacy protection solutions, such as legal privacy requirements and algorithms for obfuscating personal information, e.g., visual privacy filters [2], as well as, social awareness of threats to privacy can also increase security of personal information and potentially reduce the vulnerability of biometric systems.In this chapter, however, we focus on presentation attacks detection in voice biometrics, i.e., automatic speaker verification (ASV) systems. We discuss vulnerabilities of these systems to presentation attacks (PAs), present different state of the art 1

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Audio recapture detection using deep learning

Cited by 12 publications

References 17 publications

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

On the use of convolutional neural networks for speech presentation attack detection

Presentation attack detection in voice biometrics

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