A Machine Learning Model to Detect Fake Voice

Rodríguez-Ortega, Yohanna; Ballesteros, Luis; Renza, Diego

doi:10.1007/978-3-030-61702-8_1

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Even after a thorough search, we were not able to find a reference method that could benchmark the research problem we are after. There are methods, like [66], but they are non-blind, i.e., to say these methods require a recording of the original voice. One may argue in favor of spoofing detection literature, especially the one-class methods, like OC-Softmax [67] and its variants [68][69][70], but the main method concerns machine generated speech.…”

Section: Benchmarking Resultsmentioning

confidence: 99%

Faked speech detection with zero prior knowledge

Al Ajmi,

Hayat,

Al Obaidi

et al. 2024

Discov Appl Sci

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Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone, thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word ‘blindly’ refers to the ability to detect mimicked audio without references or real sources. We propose a deep neural network following a sequential model that comprises three hidden layers, with alternating dense and drop out layers. The proposed model was trained on a set of 26 important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English). For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy, as we were able to get at least $$94\%$$ 94 % correct classification of the test cases, as against the $$85\%$$ 85 % accuracy in the case of human observers.

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Section: Benchmarking Resultsmentioning

confidence: 99%

Faked speech detection with zero prior knowledge

Al Ajmi,

Hayat,

Al Obaidi

et al. 2024

Discov Appl Sci

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show abstract

“…Handcrafted features‐based model requires manual extraction of features that is a time‐consuming process. Rodriguez‐Ortega et al (2020) detected fake audio in two facets. Fake dataset for audio was generated using imitation method by extracting entropy features of fake and real sample audios.…”

Section: Deepfake Audio Detectionmentioning

confidence: 99%

“…Moreover, audio deepfakes have become common tools to commit forgeries, allowing a person to impersonate someone else and spread of rumours and falsification. Three main types of audio deepfakes popular now‐a‐days are: Imitation based (Rodríguez‐Ortega et al, 2020), synthetic based (Tan et al, 2021), and replay based (Garrido et al, 2015) as shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

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Review of audio deepfake detection techniques: Issues and prospects

2023

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In the past years, multimedia content has improved in realism and plausibility owing to the development of deep learning techniques, particularly the generative adversarial networks and variational auto‐encoders. Though digital content, especially digital movies shot from a certain viewpoint gives a true representation of reality, yet the ubiquitous usage of content manipulation techniques casts doubt on its veracity. Deepfaking an AI based tampering technique, is able to map facial and acoustic features of a source person onto the target with an intention to make target say or enact the things that has not happened in real. Numerous approaches have been proposed in the literature for detection of image and video deepfakes. With technological advancement, researchers have also started to examine audio deepfakes and ways to detect them. As there is currently no comprehensive overview of audio deepfake generation and detection techniques, this paper aims to provide a survey of the relevant literature in this area. This survey paper intends to help research fraternity about the available audio generation and detection approaches for design of reliable detection models in future to classify fake and real audios.

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“…Traditional ML methods are commonly used in the identification of fake audios. A study [107] created a own fake audio dataset by extracting entropy features using an imitation technique named the H-Voice dataset [103]. To distinguish between the fake and real audio, the study used ML model LR.…”

Section: Deepfake Audio Detection Techniquesmentioning

confidence: 99%

A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

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A Machine Learning Model to Detect Fake Voice

Cited by 11 publications

References 15 publications

Faked speech detection with zero prior knowledge

Faked speech detection with zero prior knowledge

Review of audio deepfake detection techniques: Issues and prospects

A Review of Image Processing Techniques for Deepfakes

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