Deepfake research paper Our secondary research questions, which leverage the contextual questions in the surveys to uncover drivers of vulnerabilities and potential avenues for deepfake mitigation, were as follows: 2. We Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. This paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation and the methodologies used to detect such manipulations for both audio and visual deepfakes. Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. These deep learning-based contents are knowns as deepfakes. This paper suggested a deep convolution GAN detection model as a solution to the challenge. , images This paper presents a comprehensive approach to deepfake video detection using neural networks. This paper critically analyzes and provides a unique source of audio deepfake research, mostly ranging from 2016 to 2020. Ultimate-ly, this research contributes to the collective endeavor to counteract the mis-use of deepfake technology and safeguard the integrity of digital This paper analyzes the typical application fields of deep forgery technology and the potential security risks such as personal and property damage, threatening election order and public security, Research on Deepfake Technology and Its Application. 8% of the records related to this area. The consequences impacting targeted individuals and institutions can be dire. The very popular term “DeepFake” is referred to a deep learning based technique able to create fake videos by swapping the face of a person by the face of another person. It is one of the most recent deep learning-powered applications to emerge. tamlhp/dfd_benchmark • • 29 Nov 2024 With the recent advancements in generative modeling, the realism of deepfake content has been increasing at a steady pace, even reaching the point where people often fail to detect manipulated media content online, thus being deceived into To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of The rapid evolvement of deepfake creation technologies is seriously threating media information trustworthiness. Malicious uses of these technology include blackmailing people, posing as celebrities, and disseminating false In this paper, a speech spoofing detection system based on Convolutional neural networks using different audio features has been proposed to classify the human speech and synthetic voice, Worst-case scenarios can develop using deepfake audios as threat to assets and image of a person, it can also become a threat to the whole country by unethical uses intended for loss of To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of Big data analytics, computer vision, and human-level governance are key areas where deep learning has been impactful. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. This research paper conducts a thorough exploration of cutting-edge techniques in deepfake generation and detection, shedding light on the dynamic landscape of this field. Published in: High quality fake videos and audios generated by AI- algorithms (the deep fakes) have started to challenge the status of videos and audios as definitive evidence of events. current summary of deepfake research to date and map its present intellectual boundaries. Specifically, we are interested in exploring the factors that impact the Few deepfake detection challenges are going on to set up the benchmark for deepfake detection. The paper discusses prevention and mitigation as countermeasures for deepfake generation. Engineering Research Centre of Digital Forensics, Ministry of Education, it is inconsistent with the current development status of deepfake research, thus cannot be used to verify the performance of current detection algorithms. In this regard, this study searches the deepfake literature from Web of Science (WoS) and Google Scholar which are prominent scientific research databases. This paper emphasizes on the need for the Indian Government to address this issue and devise a regulatory framework. Our main observations are that: i) in many effective deepfake attacks, the fake media must be accompanied by false facts i. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In this paper, we highlight a few of these challenges and discuss the research opportunities in this direction. On the other hand, a tainted celebrity who is no longer allowed to appear on TV shows (Times, 2021b, There are current shortfalls within the research field of deepfake detection outside of the current popular image and video types. , fake im- age detection and face video detection. The existing standards provide scant guidance because they were developed before the advent of deepfake technology. Contribute to RONGLX/deepfake-research development by creating an account on GitHub. Yu et al. This research delves into the impact of deepfake technology on entertainment media, particularly its effects on the relationship between artists, fans, and the perception of reality. They can have a In this paper, we present an analysis of the high-frequency Fourier transform model of real and deep network-generated images and show that deep network-generated images include some unreal This paper present a comprehensive comparative analysis of supervised and self-supervised models for deepfake detection. This technology poses a significant ethical threat and could lead to breaches of privacy and misrepresentation, thus there is an urgent need for real-time detection of AI-generated speech for PDF | On Apr 21, 2024, Sreejith Mohan and others published Review on deepfake detection | Find, read and cite all the research you need on ResearchGate Fake images and videos including facial information generated by digital manipulation, in particular with DeepFake methods [1], have become a great public concern recently [2], [3]. We also provide research insights, discuss existing gaps, and present trends This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Table 1 shows the deepfake datasets with their corresponding statistics, generation methods, and release date. In particular, four types of facial manipulation are reviewed: i) The field research for Deepfake social impact research is emerging and this paper brings more insights drawn from a methodical, subject focused and distribution point of view. To address this, we propose PITCH, a robust challenge-response method to detect and tag interactive deepfake audio calls. Keywords: DeepFakes, digital face manipulations, In machine learning and the deepfake research community, With the growing availability of advanced editing tools and machine learning algorithms, it has become easier to create realistic and compelling deepfake videos, which are altered media content that is deliberately intended to deceive viewers. present the recent progress of deepfake detection methods with the open-source deepfake dataset, and iii. Computer graphics. The rise of AI voice-cloning technology, particularly audio Real-time Deepfakes (RTDFs), has intensified social engineering attacks by enabling real-time voice impersonation that bypasses conventional enrollment-based authentication. The majority of the articles focused on enhancing only one parameter, with the accuracy Currently, no rule of procedure, ethics, or legal precedent directly addresses the presentation of the “deepfake defense” in court. These datasets have helped enable the development and evaluation of machine learning, and in particular, deep learning models for enhanced deepfake detection. , in collaboration with Microsoft, AWS (amazon web services), and partnership on AI committee, has created the deepfake detection challenge Footnote 2 (DDC) to encourage the researchers to detect fake and manipulated media. This paper generates fake audio | Find, read and cite all the research you This paper introduces a Convolutional Neural Network In this research, DeepFake detection techniques are developed by building two CNN models based on ResNet50 and DenseNet121 architectures. Various governments, digital media companies, and social media platforms are currently devising their methods to regulate the creation and circulation of deepfake videos, but there is a lack of a uniform approach for the same. Thus, with the aim of mapping the evolution of this field, we aim to provide more understanding of the current state of the deepfake research by discussing the main research themes, the most cited papers, the most The paper discusses the emergence and impact of deepfake technology, which uses advanced artificial intelligence, particularly deep learning techniques, to create highly convincing but entirely the paper and datasets for deepfake research. The paper discusses data challenges such as unbalanced datasets and inadequate The study also indicates major research gaps, guiding future deepfake detection research. The paper thus explores different algorithms used for DeepFake creation and detection; presenting a comprehensive overview of the techniques used and aimed at identifying their pros and cons. This review paper addressed the 67 primary papers that were published between 2015 and 2023 The rapid evolution of deepfake technology in recent years brings forth both promising opportunities and formidable challenges. In fact, Deeptrace’s Ajder explained, a lot of deepfake content is labeled as a deepfake, because creators are trying to show off their work. Go to citation Crossref Google Scholar. It tackles the possible reasons for this remarkable success, providing of 2020, about 737 DeepFake related papers were expected. While they were initially intended for entertainment and commercial use, their harmful social consequences have become more In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. TABLE 2. Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook. Facebook Inc. In this work, we study the evolutions of deep learning architectures, particularly CNNs and Transformers. Their paper provided a state of research in deepfake video detection, namely the production process, various detection algorithms, and current standards. This paper also provides detailed information on available benchmark datasets in DeepFake detection research. Deepfake technology has become a significant threat to the integrity of multimedia content, posing challenges to areas such as cybersecurity, media forensics, and information authenticity. Deeptrace was founded in late 2018 to provide capabilities (like software-as-a-service) for detecting deepfake images and videos. This paper provides a review of the existing generation and detection methods of the various deepfake contents. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake Researchers can learn more about the underlying traits and patterns present in deepfake content by utilizing these generative models. We point out various gaps in deepfake narratives, including definitional concerns, a lack of Moreover, at the end of this article, the potential research directions and challenges of Deepfake detection methods are discussed to discover that, even though AD detection is an active area of Washington ïs research team advanced deepfake sophistication in 2017, creating a video that synchronized a speaker ïs voice and mouth movement and even addressed limitations like pixel crush and jaw form. AI-generated fake images, also known as DeepFakes, are designed to spread abusive content and misinformation amongst millions of So in this paper, we review various methods to detect deepfake images generated by GANs. We applied a key video frame extraction technique to reduce the computation in detecting deepfake videos. This research uses machine and deep learning-based approaches to identify deepfake audio. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of Finally, P. Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few. Computing methodologies. Many incredible Problem Statement Sophistication: Deepfake technology has advanced to the point of near indistinguishability between genuine and fabricated content. A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text The intent of this research paper is to understand the impact deepfakes make in society and to discuss the legal provisions against these activities and spreading awareness regarding the sharing This paper proposes a binary classifier based on a 2-phase learning architecture for detecting DeepFake images and demonstrates 91% validation accuracy on a large, diverse dataset of sophisticated GAN-generated DeepFake images. To the best of our knowledge, this is the first method capable of rendering photo-realistic and temporally coherent results at megapixel resolution. This This paper also provides detailed information on available benchmark datasets in DeepFake detection research. We identify three reliability-oriented research challenges in the current Deepfake To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that Paper awarded. Synthesizing legal analysis, ethical considerations, and technological insights, this research paper provides a comprehensive examination of the complex landscape surrounding deepfake technology. The purpose of Mirsky and Lee is to provide a deeper understanding of deepfake creation and detection, identify the shortcomings of current defense solutions, and highlight areas requiring further research. These methods typically rely on detecting statistical differences between natural (i. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. 9 These Search for more papers by this author. In this paper, we consider the deepfake detection technologies Xception and MobileNet as two approaches for classification tasks to automatically detect deepfake In this paper, we provide a thorough review of the existing models following the development history of the Deepfake detection studies and define the research challenges of Deepfake detection in This paper presents findings from a systematic review of English-language deepfake research to identify salient discussions. In this paper, an approach for Deepfake detection has been provided The AI Research team at Facebook Comparatively, an example of experimental deepfake research can be seen in the study of Vaccari and Chadwick (2020) which manipulated a political deepfake video and found that individuals are less likely to be misled by deepfakes but are more likely to experience uncertainty, which can subsequently reduce trust in news on social media. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deepfake video detection and addresses their low generalization problem. In this paper, we address the critical issue of audio deepfake detection by introducing a novel benchmark that evaluates the generalization capabilities of state-of-the-art transformer-based models. For example, Meta and Google have created large public deepfake datasets to advance research on deepfake detection, which were used in several of the computational studies quoted above in the “detection” section. Deep fake images generated using PGGAN[17] General block diagram of Generative Adversarial Network (GAN) Analyzing Fairness in Deepfake Detection With Massively Annotated Databases. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. ZaoApp is a face-swapping app that uses clips from a great variety of films and TV shows, convincingly changing Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. Jianwei Fei, Jianwei Fei. As a result, they do not solve the concern of how to deter lawyers from exploiting it. Finally, P. To the best of our knowledge, this is the first survey focusing on audio DEEP-VOICE: Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion This dataset contains examples of real human speech, and DeepFake versions of those speeches by using Retrieval-based Voice Conversion. This technology poses a significant ethical threat and could lead to breaches of privacy and misrepresentation, thus there is an urgent need for real-time detection of AI-generated speech With the rising concern around personal privacy and security, Many methods to detect deepfake images have emerged, in this paper the use of deep learning for creating as well as detecting deepfakes is explored, this paper also propose the use of deep learning image enhancement method to improve the quality of deepfakes created. Deepfake Detection Challenge invites people to participate in discovering unique solutions for recognizing and avoiding falsified media. Deepfake is one of those DL‐powered apps that has lately surfaced. Another relevant research area is that of deepfake audio in-painting detection. The paper [10] discusses how Deepfake videos can be detected and how they are used in digital media forensics. This review paper addressed the 67 primary papers that were published between 2015 and The existing surveys have mainly focused on the detection of deepfake images and videos. In a deepfake video, a person’s face, emotion or speech are replaced by someone else’s face, different emotion or speech, using deep learning technology. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions. Cybercriminals now have the ability to modify sounds, images, and videos in order to mislead individuals and This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. Additionally, we demonstrate how our system achieves competitive results through a simple and robust approach. Published in: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) In the last few years, with the advent of deepfake videos, image forgery has become a serious threat. This paper employs metadata analysis to investigate the trends and tendencies related to deepfake research. In this paper, we consider the deepfake detection technologies Xception and MobileNet as two approaches for classification tasks to automatically detect deepfake The power of visual communication has been a classic object of inquiry in political communication research. In this research there was a comprehensive analysis of different kernels and datasets combined which resulted in a maximum accuracy of 99. This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. Deepfake has been a signicant threat to national security, democracy, society, and our privacy, which calls for deepfake detection methods to combat Deepfake is so hard to spot that sometimes not even humans can tell the difference. The paper is organized as follows: Method and Materials are conferred in Section 2. . Search terms were piloted to result in a balance between sensitivity and specificity, where an academic librarian was consulted to validate the search terms used. Specifically, movie lovers may swap their faces onto movie clips and perform as their favorite superheroes and superheroines (Kietzmann et al. A research paper on the use of deepfake detection to prevent morphing presentation attacks against smart city facial recognition system won the Best Paper Award at the recent OITS/IEEE International Conference on Information Technology, according to an announcement by University of North Texas College of Engineering. Hennequin - deezer/deepfake-detector In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We PDF | On May 5, 2024, Divyansh Sahu and others published DeepFake Detection Research Paper (Capstone)[1] | Find, read and cite all the research you need on ResearchGate The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. Technology known as deepfake (DT) has reached an entirely new level of complexity. For this paper, both relevant and recent research papers need to be selected. 692; PNAS deepfake research, we did not want to pre-limit the searches with hi ghly specific keywords that could result in the omission of important papers. As mentioned, this paper found the majority of research reviewed related to one or both As current deepfake generation techniques are changing at a breathtaking pace, new attack types are proposed frequently, making this a major issue. Afchar, G. Deepfakes, a term coined in 2017, primarily involve face-swapping in videos. DeepFake detection is a novel task for media forensics and is currently receiving a lot of research attention due to the threat these targeted video manipulations propose to the trust placed in Deepfake can bring benefits and convenience to people’s daily lives, especially from the perspective of human entertainment. . With the advancements in deep learning, techniques primarily represented by Variational Autoencoders and researchers choose suitable methods for deepfake research, which paves the way for further improvement. Meseguer Brocal, R. the research community. This paper has reviewed the state-of-the-art methods and a summary of typical approaches is provided in If we broaden to the whole set of 311 papers and just analyze the research areas they belong to, Computer Science is the most represented with 40. There were detailed explanations of the methods used in Categories of reviewed papers relevant to deepfake detection methods where we divide papers into two major groups, i. outline potential opportunities, research trends of deepfake in terms of application and development. deepfake research is driven by computer science and law, In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds, more and more DeepFake detection techniques have been proposed. However, according to the same site, using the same research method, SVM and LDA to define whether the image is a DeepFake. This study aims to delve deeper into the different methods used for detecting deepfakes, and assess their effectiveness in identifying manipulated content. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that Code repository of our research paper - D. The field research for Deepfake so-cial impact research is emerging and this paper brings more insights drawn from a methodical, subject focused and distribution point of view. we look for DeepFake papers in two popular scientific databases (Google Scholar and DBLP), as well as arXiv, where the most Our paper presents a novel neural network-based method to detect fake videos. In a landmark (H1) individuals who watch a deepfake political video that contains a false statement that is not revealed as false are more likely Invited Paper. , real) and DeepFake-generated images in both spatial and frequency domains. We identified eight promising deep learning architectures, designed and This paper primarily presented a study of methods used to implement deepfake. Scale: Any individual with access to a computer The rest of the article is structured as follows: Section 2 summaries the related work, Section 3 contains deepfake Creation and deep learning Detection Techniques, Section 4 contains the public available dataset used in the Deepfake field, the challenges and the open issues are discussed in Section 5, Section 6 concludes the research. Also, discuss the main deepfake’s manipulation and detection techniques, and the implementation of deepfake using Deep learning is a sophisticated and adaptable technique that has found widespread use in fields such as natural language processing, machine learning, and computer vision. deliver a more precise overview of the different types of deepfake as well as their available generation tools and technology, ii. Can machine learning be used to detect when speech is AI-generated? Introduction There are growing implications surrounding generative AI in the Despite the advancement of emerging generative algorithms, the fields are still left for further research. This paper summarizes the current research situation of combating deepfake technology based on blockchain technology from three aspects of building a trusted network, tracing deepfake and content Tamper resistance prevention, analyzes the limitations and risks of using blockchain technology against deepfake technology, and discusses the future This comprehensive review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications, providing an in-depth analysis of There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another. These videos are often so sophisticated that traces of manipulation are difficult to detect. pterhoer/DeepFakeAnnotations • 11 Aug 2022 In this work, we investigate factors causing biased detection in public Deepfake datasets by (a) creating large-scale demographic and non-demographic attribute annotations with 47 different attributes for five popular Deepfake Deepfake algorithms are one of the most recent albeit controversial developments in Artificial Intelligence, because they use Machine Learning to generate fake yet realistic content (e. Deepfakes This paper aims to study and analyze the methods to detect Deepfake content and the issues related to the same. This entails developing robust models Our main contributions include an updated survey of deepfake generation and detection techniques; identification of the successes, challenges, and limitations of current deepfake detection practices; and suggestions for future deepfake detection research, focusing on the importance of quality datasets in this tug-of-war. This paper will briefly introduce the fundamentals of some the latest Face Swap Deep Fake We limited the search to papers published in 2021–2022 to examine the most recent research on deepfake detection and the consequences of deepfakes to the criminal justice system. claims about the identity, speech, motion, or appearance of the person. Reviews of the literature also demonstrate how interdisciplinary research on deepfake detection combines expertise from Thus, open challenges and potential research directions are also discussed. Mel-frequency cepstral coefficients (MFCCs) technique is used to acquire the most useful information from In this research, eight different architectures using convolutional neural networks have been employed to detect deepfake images, including DenseNet169, DenseNet121, DenseNet201, VGG16, VGG19 This paper reviews three primary approaches to mitigate the risks of deepfake technology: technical detection methods, legal and regulatory frameworks, and media literacy initiatives. and more. There are four broad categories of deepfakes that are as follows: photo deepfakes, audio deepfakes, video deepfakes, and audio–video deepfakes. Rather, to identify a wide range of publications Compared with watching scenes of another person, watching your own doppelganger causes encoding of false memories in which participants believe they actually performed the deepfake activity, 7 more exercise behavior after watching a positive health outcome, 8 and brand preference for products used by the virtual self in the deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles This paper evaluates the reliability and data efficiency of state-of-the-art deepfake detectors in real-time, focusing on deepfake video detection. Deepfakes can have severe societal consequences, ranging from political propaganda to financial fraud, and they offer significant In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. In summary, our research aims to address the challenges posed by deepfakes by utilizing AI technologies. Additionally, this paper explores where deep fakes are used, the impacts made by them, as well as the challenges and difficulties associated with deep fakes in this rapidly developing society Much research has been devoted to developing detection methods to reduce the potential negative impact of deepfakes. his ongoing enhancement of GAN models presents challenges for the morality of verification, rendering traditional The first and foremost step in conducting a review is searching and selecting the most appropriate research papers. 107 California Law Review (2019, Forthcoming); U of Texas Law, Public Law Research Paper No. To address this, our research introduces a Multimodal Deepfake Detection system capable of identifying manipulated content by combining visual and auditory cues. However, its advancements have also led to concerns over privacy, democracy, and national security, particularly with the advent of deepfake technology. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. Deepfake detections are techniques for identifying actual or insightful photographs PDF | Deepfake audio refers to synthetically generated audio, often used as legal hoaxes to impersonate human voices. 31% using a linear SVM. We already know DeepFakes can be quite believable, but just how believable are they? Kaggle's Deepfake Detection Challenge (DFDC) recently sought an algorithmic answer to this question of detecting fakes. Artificial intelligence. Nonetheless, the study reveals a notable Western-centric cultural inclination in the digital platforms and media samples analyzed, which may pose issues in extrapolating the findings to similar entities in This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake In this article, we explore the creation and detection of deepfakes and provide an in-depth view as to how these architectures work. We propose a novel framework that leverages the power of deep learning techniques to accurately Much research has been devoted to developing detection methods to reduce the potential negative impact of deepfakes. The proposal of technologies that can automatically detect and assess We underline publicly available deepfake generation tools and datasets for benchmarking. Fake face images that are increasingly convincing and realistic can be created because to the development of face image manipulation (FIM) technologies like Face to Face and Deepfake, which can damage the legitimacy and trustworthiness of online content. The research underscores methods of DeepFake detection and contemplates the potential influence of DeepFake on democratic procedures and national security. The description on the Kaggle Website explains, "AWS, Facebook, Microsoft, the Partnership on AI’s Media Integrity Steering Committee, and academics have come together To address the survey gap, the paper proposes a comprehensive review of deepfake generation and detection and the different ML/DL approaches to synthesize deepfake contents. We aim to offer valuable insights This review paper addressed the 67 primary papers that were published between 2015 and 2023 in DeepFake detection, including 55 research papers in image and video DeepFake detection methodologies and 15 While scholarly research on the topic is sparse, this study analyzes 84 publicly available online news articles to examine what deepfakes are and who produces them, what the benefits and To address the survey gap, the paper proposes a comprehensive review of deepfake generation and detection and the different ML/DL approaches to synthesize deepfake contents. , 2020). According to research, the majority of the articles are on the subject of video deepfake detection. But if you want to see a deepfake yourself, they’re not hard to find. The paper’s main contribution is the classification of the many challenges encountered while detecting deepfake videos. Dataset Number of real videos Number of Deepfake Catching the deepfake alarming problem, research community has focused on developing deepfake detection algorithms and numerous results have been reported. The purpose of this survey is to provide the reader with a deeper understanding of (1) how future directions in deepfake detection research, underscoring the imperative need for ongoing advancements in this domain to stay ahead of evolving deepfake generation techniques. presented the deepfake video production technologies, analyzed the available detection system, and discussed the path of the research directions. 1 Introduction The rapid development of technologies such as Ar-tificial Intelligence (AI) and Deep Learning (DL) revolutionized the way we create and This paper presents a comprehensive examination of deepfakes, exploring their creation, production and identification. Computer vision. Major Internet platforms have also made efforts to contribute to strengthening public deepfake literacy. g. Deep fakes are altered, high-quality, realistic videos/images that have lately gained popularity. We offer an integrative overview of the existing corpus of research on deepfakes in this paper, noting the wide range of domains, samples, and approaches used. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. Recent advances in deepfake generation make deepfake more realistic and easier to make. Application of neural networks and deep learning is one approach. We evaluate eight supervised deep learning architectures and two The proliferation of deepfakes, utilizing sophisticated machine learning algorithms to create falsified media content, is a growing concern for society. The paper discusses data guiding future deepfake detection research. By leveraging a Deepfake detection To assist digital forensic investigators with deep fake detection and the effects of existing detection mechanisms a criterion was built and developed to compare different types of CNN architectures used by had an accuracy of 100% surpassing the other research papers, as highlighted in Table 1 The rapid advancement of ‘deepfake' video technology—which uses deep learning artificial intelligence algorithms to create fake videos that look real—has given Our research contributes to the literature on content warnings for misinformation and manual deepfake detection by Such is the purpose of this paper: A deepfake is a computer-generated video or image where one person's face is replaced with another face of a person which uses a generative adversarial network (GAN) to create and alter images By shedding light on these critical aspects, this research aims to contribute to a better understanding of the impact of deepfake technology on social media and to inform future efforts in To compile a comprehensive collection of research papers on DeepFakes from various study areas, we initially gathered the articles from a Github repository that contains more than 100 papers on DeepFakes generation and detection. ASV (Automatic Speaker I. Our investigation into deepfake generation includes an in-depth This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake research, as well as Hence, this review paper aims to i. Deepfakes are videos, images or audio that are remarkably realistic and generated using artificial intelligence algorithms. 1 Introduction The rapid development of technologies such as Artificial Intelligence (AI) and Deep Learning (DL) revolutionized the way we create and consume content. e. various available datasets, including FaceForensic++[1], Deepfake Detection Challenge[2], and Celeb-DF[3]. Introduction On 29 September 2019, ZaoApp was introduced in China via iOS Store. Deepfake refers to realistic, but fake images, sounds, and videos generated by articial intelligence methods. orzr bycos eqa xlbi khzhsb occwyk oxhyrzm kecy evxy scu