Deep4SNet: deep learning for fake speech classification

Ballesteros, Miguel; Rodríguez-Ortega, Yohanna; Renza, Diego; Arce, Gonzalo R.

doi:10.1016/j.eswa.2021.115465

Cited by 35 publications

(9 citation statements)

References 22 publications

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“…Most of the applications of audio are on spoof and deepfake [4]. Audio-based learning techniques for fake news detection include [11], [98], [101], [104] E. VIDEO A video is a continuous or moving visual representation of a scene or sequence of events. Usage of videos for the detection of fake news and harmful languages are also not as common as text.…”

Section: Audiomentioning

confidence: 99%

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A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Festus Ayetiran,

Özgöbek

2024

IEEE Access

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The detection of fake news and harmful languages has become increasingly important in today's digital age. As the prevalence of fake news and harmful languages continue to increase, so also is the correspondent negative impact on individuals and the society. Researchers are exploring new techniques to identify and combat these issues. Deep neural network (DNN) has found a wide range of applications in diverse problem domains including but not limited to fake news and harmful languages detection. Fake news and harmful languages are currently increasing online and the mode of dissemination of these contents is fast changing from the traditional unimodal to multiple data forms including texts, audios, images and videos. Multimedia contents containing fake news and harmful languages pose more complex challenges than unimodal contents. The choice and efficacy of the fusion methods of the multimedia contents is one of the most challenging. Our area of focus is multimodal techniques based on deep learning that combines diverse data forms to improve detection accuracy. In this review, we delve into the current state of research, the evolution of deep learning techniques that have been proposed for multimodal fake news and harmful languages detection and the state-of-the-art (SOTA) multimedia data fusion methods. In all cases, we discuss the prospects, relationships, breakthroughs and challenges.

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Section: Audiomentioning

confidence: 99%

“…Vine was an American short-form video hosting service which is no longer in existence11 https://www.bitchute.com/…”

mentioning

confidence: 99%

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Festus Ayetiran,

Özgöbek

2024

IEEE Access

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“…Another work on this subject consisted in the analysis of histograms for the detection of fake voices [4]. This analysis uses computer vision with CNN to classify histograms according to their nature [5]. However, although the approach is interesting, the model turns out to be non-scalable and very affected by the data transformation process [2].…”

Section: Deepfake Audio Detection Modelsmentioning

confidence: 99%

Evaluating Acoustic Parameters for DeepFake Audio Identification

Djiré,

Sabané,

Kabore

et al. 2023

2023 IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI)

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The progress made in the field of machine learning applied to signal processing offers interesting perspectives in terms of technological evolution but also causes some troubles in terms of ethics and security. For example, we are witnessing the emergence of audio deepFakes used to orchestrate scams. However, although the tools used in the generation of these deepFake audios show good results which can sometimes produce audios that seem to be confused with real audio, it is not impossible to dissect them. In order to detect them, many methods exist, in particular the analysis of the acoustic parameters which can attest to the authenticity of an audio extract. These parameters include energy, power, pitch, signal spectrum, cepstral coefficients, etc. However, these acoustic parameters are numerous and not all of them are suitable for detecting deepFake audio. This paper presents a comparative review of acoustic parameters useful in detecting DeepFake audio. Among them, we highlight the relevance of the study of cepstral parameters such as MFCC compared to other acoustic parameters such as mel-spectograms. The objective is to provide reliable leads in the detection of deepFake audio.

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“…In recent years, deep learning‐based artificial intelligence (AI) technologies have made tremendous progress in many pattern recognition tasks such as object detection 15 , 16 and speech recognition. 17 , 18 Convolutional neural networks (CNNs), a subtype of deep learning, have attracted a lot of attention due to their breakthrough performance, especially in image and video processing. CNN‐based models provide effective solutions for medical images such as early detection of various diseases, boundary tracking of irregularities, and quantitative assessment of lesions.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID‐19 lung infections

Polat

2022

Int J Imaging Syst Tech

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Coronavirus disease (COVID‐19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID‐19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT‐PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time‐consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre‐trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state‐of‐the‐art segmentation methods was also performed. The experiments were carried out using a fivefold cross‐validation technique on a publicly available database containing 100 single‐slice CT scans from >40 patients with COVID‐19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross‐validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

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Deep4SNet: deep learning for fake speech classification

Cited by 35 publications

References 22 publications

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Evaluating Acoustic Parameters for DeepFake Audio Identification

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID‐19 lung infections

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