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

Korshunov, Pavel; Gonçalves, Alim P. C.; Violato, Ricardo Paranhos Velloso; Simões, Flávio; Marcel, Sébastien

doi:10.1109/isba.2018.8311474

Cited by 18 publications

(5 citation statements)

References 21 publications

(34 reference statements)

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“…The Portuguese language BioCPqD-PA dataset [71] was collected by recording 222 people in a variety of environmental conditions, and is comprised of 27,253 authentic recordings and 391,687 samples which have been subjected to a presentation attack. One laptop was used with 24 different setups consisting of 8 loudspeakers and 3 microphones, while another single laptop was used to capture real data.…”

Section: Other Publicly Available Datasets and Asv Challengesmentioning

confidence: 99%

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

et al. 2023

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With the advent of automated speaker verification (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofing attacks to fool those same systems. Various countermeasures have been proposed to detect these spoofing attacks, but current offerings in this arena fall short of a unified solution. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classified, and qualitative and quantitative comparisons of state-of-the-art (SOTA) countermeasures should be performed to assess the effectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, and end-to-end spoofing countermeasure solutions to detect logical access (LA) attacks, such as speech synthesis (SS) and voice conversion (VC), and physical access (PA) attacks, i.e., replay attacks. Additionally, we review integrated and unified solutions to voice spoofing evaluation and speaker verification, and adversarial and anti-forensic attacks on both voice countermeasures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofing countermeasures are presented, the performance of these countermeasures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the Voice Spoofing Attacks and Countermeasures ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproducibility of the results, the code of the testbed can be found at our GitHub Repository * ).

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Section: Other Publicly Available Datasets and Asv Challengesmentioning

confidence: 99%

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

et al. 2023

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“…There are other publicly available databases. One is the AVspoof database [50] and its extension called VoicePA [49]. Another one is the Spoofing and Antispoofing (SAS) [105] database.…”

Section: Datasets and Campaignsmentioning

confidence: 99%

A Practical Guide to Logical Access Voice Presentation Attack Detection

Wang¹,

Yamagishi²

2022

Preprint

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Voice-based human-machine interfaces with an automatic speaker verification (ASV) component are commonly used in the market. However, the threat from presentation attacks is also growing since attackers can use recent speech synthesis technology to produce a naturalsounding voice of a victim. Presentation attack detection (PAD) for ASV, or speech anti-spoofing, is therefore indispensable. Research on voice PAD has seen significant progress since the early 2010s, including the advancement in PAD models, benchmark datasets, and evaluation campaigns. This chapter presents a practical guide to the field of voice PAD, with a focus on logical access attacks using text-to-speech and voice conversion algorithms and spoofing countermeasures based on artifact detection. It introduces the basic concept of voice PAD, explains the common techniques, and provides an experimental study using recent methods on a benchmark dataset. Code for the experiments is open-sourced.

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“…At such short distances, certain acoustic features can be used to identify the sound source of the speaker, e.g., in [25], the authors use the "pop noise" caused by breathing to identify a live speaker. Other efforts [26,27] do not explicitly use close-distance features, but the databases they use to develop their defense strategies were recorded at close distances [23,28,29], and therefore, these approaches may also implicitly use close-distance features. In contrast, with the help of far-field speech recognition techniques, modern VCSs can typically accept voice commands from rather long distances (i.e., several meters) [10].…”

Section: Comparing Vcs and Asv Protectionmentioning

confidence: 99%

ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

Gong

Yang

Huber³

et al. 2019

Interspeech 2019

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This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits. Specifically, the database contains recordings from four systems (each with a different microphone array) in a variety of environmental conditions with different forms of background noise and relative positions between speaker and device. To the best of our knowledge, this is the first publicly available database 1 that has been specifically designed for the protection of state-of-the-art voice-controlled systems against various replay attacks in various conditions and environments.

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

Cited by 18 publications

References 21 publications

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

A Practical Guide to Logical Access Voice Presentation Attack Detection

ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

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