Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution

Kwak, Il-Youp; Kwag, Sungsu; Lee, Junhee; Jeon, Youngbae; Hwang, Jeonghwan; Choi, Hae-Jin; Yang, Jonghoon; Han, Soyul; Huh, Jun Ho; Lee, Choong-Hoon; Yoon, Ji Won

doi:10.1109/access.2023.3275790

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…Class weights were experimentally determined through a grid search ranging from 2 to 6, and the final selected value was 3. In terms of model selection, both LCNN and ResMax [38,39] architectures were considered, and while showing similar performance, the simpler structure of LCNN led us to choose it for our study. The given PCG data had three labels for the noise categories: "present", "absent", and "unknown".…”

Section: Implementation Detailsmentioning

confidence: 99%

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning

Han,

Jeon,

Gong

et al. 2023

Biology

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In this study, we constructed a model to predict abnormal cardiac sounds using a diverse set of auscultation data collected from various auscultation positions. Abnormal heart sounds were identified by extracting features such as peak intervals and noise characteristics during systole and diastole. Instead of using raw signal data, we transformed them into log-mel 2D spectrograms, which were employed as input variables for the CNN model. The advancement of our model involves integrating a deep learning architecture with feature extraction techniques based on existing knowledge of cardiac data. Specifically, we propose a multi-channel-based heart signal processing (MCHeart) scheme, which incorporates our proposed features into the deep learning model. Additionally, we introduce the ReLCNN model by applying residual blocks and MHA mechanisms to the LCNN architecture. By adding murmur features with a smoothing function and training the ReLCNN model, the weighted accuracy of the model increased from 79.6% to 83.6%, showing a performance improvement of approximately 4% point compared to the LCNN baseline model.

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Section: Implementation Detailsmentioning

confidence: 99%

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning

Han,

Jeon,

Gong

et al. 2023

Biology

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CloneAI: A Deep Learning-Based Approach for Cloned Voice Detection

Kheria,

Karani

2024

Lecture Notes in Networks and Systems

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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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Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution

Cited by 6 publications

References 46 publications

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning

CloneAI: A Deep Learning-Based Approach for Cloned Voice Detection

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

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