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Journal articles on the topic 'Deepfake'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Yasrab, Robail, Wanqi Jiang, and Adnan Riaz. "Fighting Deepfakes Using Body Language Analysis." Forecasting 3, no. 2 (2021): 303–21. http://dx.doi.org/10.3390/forecast3020020.

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Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepf

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Twomey, John, Didier Ching, Matthew Peter Aylett, Michael Quayle, Conor Linehan, and Gillian Murphy. "Do deepfake videos undermine our epistemic trust? A thematic analysis of tweets that discuss deepfakes in the Russian invasion of Ukraine." PLOS ONE 18, no. 10 (2023): e0291668. http://dx.doi.org/10.1371/journal.pone.0291668.

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Deepfakes are a form of multi-modal media generated using deep-learning technology. Many academics have expressed fears that deepfakes present a severe threat to the veracity of news and political communication, and an epistemic crisis for video evidence. These commentaries have often been hypothetical, with few real-world cases of deepfake’s political and epistemological harm. The Russo-Ukrainian war presents the first real-life example of deepfakes being used in warfare, with a number of incidents involving deepfakes of Russian and Ukrainian government officials being used for misinformation

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Sunkari, Venkateswarlu, and Ayyagari Sri Nagesh. "Artificial intelligence for deepfake detection: systematic review and impact analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 3786. http://dx.doi.org/10.11591/ijai.v13.i4.pp3786-3792.

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<p>Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its social implications in this study. We examine capsule networks' ability to detect video deepfakes and their design implications. This strategy reduces parameters and provides excellent accuracy, making it a promising deepfake defense. The social significance of deepfakes is also highlighted, underlining the necessity to understand the

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Venkateswarlu, Sunkari, and Sri Nagesh Ayyagari. "Artificial intelligence for deepfake detection: systematic review and impact analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 3786–92. https://doi.org/10.11591/ijai.v13.i4.pp3786-3792.

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Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its social implications in this study. We examine capsule networks' ability to detect video deepfakes and their design implications. This strategy reduces parameters and provides excellent accuracy, making it a promising deepfake defense. The social significance of deepfakes is also highlighted, underlining the necessity to understand them. Despit

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Athawale, Prof. S. V., Shreyash Vyawahare, Priyanshu Marodkar, Srushti Lanjewar, and Pratiksha Tawar. "Deepfake Detection Model." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 2 (2024): 195–202. https://doi.org/10.5281/zenodo.11180891.

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<em>Deepfakes are a type of synthetic media that can be used to create realistic videos of people saying or doing things they never did. This raises concerns about the potential for deepfakes to be used to spread misinformation or propaganda. In this project, we present a deepfake detection module that can be used to identify deepfakes with high accuracy. The deepfake detection module is based on a pre-trained InceptionResNetV2 model that is fine-tuned on a dataset of real and deepfake videos. The model is able to extract features from the videos that are indicative of whether they are real or

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Lad, Sumit. "Adversarial Approaches to Deepfake Detection: A Theoretical Framework for Robust Defense." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 46–58. http://dx.doi.org/10.60087/jaigs.v6i1.225.

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The rapid improvements in capabilities of neural networks and generative adversarial networks (GANs) has given rise to extremely sophisticated deepfake technologies. This has made it very difficult to reliably recognize fake digital content. It has enabled the creation of highly convincing synthetic media which can be used in malicious ways in this era of user generated information and social media. Existing deepfake detection techniques are effective against early iterations of deepfakes but get increasingly vulnerable to more sophisticated deepfakes and adversarial attacks. In this paper we

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Rajagopal, Tendral, Velayutham Chandrashekaran, and Vignesh Ilango. "Unmasking the Deepfake Infocalypse: Debunking Manufactured Misinformation with a Prototype Model in the AI Era “Seeing and hearing, no longer believing.”." Journal of Communication and Management 2, no. 04 (2023): 230–37. http://dx.doi.org/10.58966/jcm2023243.

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Machine learning and artificial intelligence in Journalism are aid and not a replacement or challenge to a journalist’s ability. Artificial intelligence-backed fake news characterized by misinformation and disinformation is the new emerging threat in our broken information ecosystem. Deepfakes erode trust in visual evidence, making it increasingly challenging to discern real from fake. Deepfakes are an increasing cause for concern since they can be used to propagate false information, fabricate news, or deceive people. While Artificial intelligence is used to create deepfakes, the same technol

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Niveditha, Zohaib Hasan Princy, Saurabh Sharma, Vishal Paranjape, and Abhishek Singh. "Review of Deep Learning Techniques for Deepfake Image Detection." International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 11, no. 02 (2022): 1–14. http://dx.doi.org/10.15662/ijareeie.2022.1102021.

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Deepfake is an advanced synthetic media technology that generates convincingly authentic yet fake images and videos by modifying a person's likeness. The term "Deepfake" is a blend of "Deep learning" and "Fake," highlighting the use of artificial intelligence and deep learning algorithms in its creation. Deepfake generation involves training models to learn the nuances of facial attributes, expressions, motion, and speech patterns to produce fabricated media indistinguishable from real footage. Deepfakes are often used to manipulate human content, especially the invariant facial regions. The s

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Tulga, Ahmet Yiğitalp. "Deepfake Interest in South Korea: A Temporal Analysis of Google Trends from 2017 to 2024." İletişim Kuram ve Araştırma Dergisi, no. 69 (March 18, 2025): 220–38. https://doi.org/10.47998/ikad.1570974.

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Deepfake technology, which utilizes artificial intelligence to generate hyper-realistically manipulated videos, images, texts, and audio, has garnered significant public and academic interest. The proliferation of deepfakes, especially in non-consensual pornography, financial fraud and political misinformation, has sparked ethical, moral, legal, and security debates worldwide. While existing research predominantly focuses on deepfake detection, legal frameworks, and their potential impact on the democratic process, few studies have examined public interest in deepfakes and the factors influenc

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Battula Thirumaleshwari Devi, Et al. "A Comprehensive Survey on Deepfake Methods: Generation, Detection, and Applications." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 654–78. http://dx.doi.org/10.17762/ijritcc.v11i9.8857.

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Due to recent advancements in AI and deep learning, several methods and tools for multimedia transformation, known as deepfake, have emerged. A deepfake is a synthetic media where a person's resemblance is used to substitute their presence in an already-existing image or video. Deepfakes have both positive and negative implications. They can be used in politics to simulate events or speeches, in translation to provide natural-sounding translations, in education for virtual experiences, and in entertainment for realistic special effects. The emergence of deepfake face forgery on the internet ha

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Krueger, Natalie, Mounika Vanamala, and Rushit Dave. "Recent Advancements in the Field of Deepfake Detection." International Journal of Computer Science and Information Technology 15, no. 4 (2023): 01–11. http://dx.doi.org/10.5121/ijcsit.2023.15401.

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A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use b

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Dr.A.Shaji, George, and George A.S.Hovan. "Deepfakes: The Evolution of Hyper realistic Media Manipulation." Partners Universal Innovative Research Publication (PUIRP) 01, no. 02 (2023): 58–74. https://doi.org/10.5281/zenodo.10148558.

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Deepfakes, synthetic media created using artificial intelligence and machine learning techniques, allow for the creation of highly realistic fake videos and audio recordings. As deepfake technology has rapidly advanced in recent years, the potential for its misuse in disinformation campaigns, fraud, and other forms of deception has grown exponentially. This paper explores the current state and trajectory of deepfake technology, emerging safeguards designed to detect deepfakes, and the critical role of education and skepticism in inoculating society against their harms. The paper begins by prov

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Ghariwala, Love. "Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2982–86. https://doi.org/10.22214/ijraset.2025.67997.

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The rise of Artificial Intelligence (AI) has opened up new possibilities, but it also brings significant challenges. Deepfake technology, which creates realistic fake videos, raises concerns about privacy, identity, and consent. This paper explores the impacts of deepfakes and suggests solutions to mitigate their negative effects. Deepfake technology, which allows the manipulation and fabrication of audio, video, and images, has gained significant attention due to its potential to deceive and manipulate. As deepfakes proliferate on social media platforms, understanding their impact becomes cru

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Bodrov, Nikolay Filippovich, and Antonina Konstantinovna Lebedeva. "The concept of deepfake in Russian law, classification of deepfake and issues of their legal regulation." Юридические исследования, no. 11 (November 2023): 26–41. http://dx.doi.org/10.25136/2409-7136.2023.11.69014.

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The article deals with the issues of legal regulation of deepfake in the Russian Federation. Legal regulation of deepfake does not keep up with the pace of development of artificial intelligence technologies. The authors emphasize that there is no definition of deepfake in the current legislation, and the existing formulations in scientific works are extremely contradictory in nature. Taking into account the pace of development of artificial intelligence technologies, it is necessary to legislate the definition of deepfake. The authors note that the classification of deepfakes is fundamentally

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Kawabe, Akihisa, Ryuto Haga, Yoichi Tomioka, Jungpil Shin, and Yuichi Okuyama. "A Dynamic Ensemble Selection of Deepfake Detectors Specialized for Individual Face Parts." Electronics 12, no. 18 (2023): 3932. http://dx.doi.org/10.3390/electronics12183932.

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The development of deepfake technology, based on deep learning, has made it easier to create images of fake human faces that are indistinguishable from the real thing. Many deepfake methods and programs are publicly available and can be used maliciously, for example, by creating fake social media accounts with images of non-existent human faces. To prevent the misuse of such fake images, several deepfake detection methods have been proposed as a countermeasure and have proven capable of detecting deepfakes with high accuracy when the target deepfake model has been identified. However, the exis

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Vinogradova, Ekaterina. "The malicious use of political deepfakes and attempts to neutralize them in Latin America." Latinskaia Amerika, no. 5 (2023): 35. http://dx.doi.org/10.31857/s0044748x0025404-3.

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Deepfake technology has revolutionized the field of artificial intelligence and communication processes, creating a real threat of misinformation of target audiences on digital platforms. The malicious use of political deepfakes has become widespread between 2017 and 2023. The political leaders of Argentina, Brazil, Colombia and Mexico were attacked with elements of doxing. Fake videos that used the politicians&apos; faces undermined their reputations, diminishing the trust of the electorate, and became an advanced tool for manipulating public opinion. A series of political deepfakes has r

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Jing, Tian Chua, and Hasbollah Mat Saad. "TRUTH DISTORTED: DEEPFAKES AND THE FIGHT FOR WOMEN'S RIGHTS." International Journal of Law, Government and Communication 10, no. 39 (2025): 33–346. https://doi.org/10.35631/ijlgc.1039023.

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Artificial intelligence is developing rapidly, and deepfake technology is one of the inventions. This study explores in depth the impact of the misuse of deepfake technology on women's rights. The findings of the study show that deepfake technology has both beneficial and harmful sides, which severely impact on the society. The study aims to analyse existing laws and their ability to protect victims from deepfake abuse, especially women. Notably, deepfakes can produce misleading content, violate privacy and dignity, and lead to reputational damage which mainly targets women and harms their rig

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Chen, Junyi, Minghao Yang, and Kaishen Yuan. "A Review of Deepfake Detection Techniques." Applied and Computational Engineering 117, no. 1 (2025): 165–74. https://doi.org/10.54254/2755-2721/2025.20955.

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With the development of deepfake technology, the use of this technology to forge videos and images has caused serious privacy and legal problems in society. In order to solve these problems, deepfake detection is required. In this paper, the generation and detection techniques of deepfakes in recent years are studied. First, the principles of deepfake generation technology are briefly introduced, including Generative Adversarial Networks (GAN) based and autoencoder. Then, this paper focuses on the detection techniques of deepfakes, classifies them based on the principles of each method, and su

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Raza, Ali, Kashif Munir, and Mubarak Almutairi. "A Novel Deep Learning Approach for Deepfake Image Detection." Applied Sciences 12, no. 19 (2022): 9820. http://dx.doi.org/10.3390/app12199820.

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Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Ki

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Godulla, Alexander, Christian P. Hoffmann, and Daniel Seibert. "Dealing with deepfakes – an interdisciplinary examination of the state of research and implications for communication studies." Studies in Communication and Media 10, no. 1 (2021): 72–96. http://dx.doi.org/10.5771/2192-4007-2021-1-72.

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Using artificial intelligence, it is becoming increasingly easy to create highly realistic but fake video content - so-called deepfakes. As a result, it is no longer possible always to distinguish real from mechanically created recordings with the naked eye. Despite the novelty of this phenomenon, regulators and industry players have started to address the risks associated with deepfakes. Yet research on deepfakes is still in its infancy. This paper presents findings from a systematic review of English-language deepfake research to identify salient discussions. We find that, to date, deepfake

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Qureshi, Shavez Mushtaq, Atif Saeed, Sultan H. Almotiri, Farooq Ahmad, and Mohammed A. Al Ghamdi. "Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media." PeerJ Computer Science 10 (May 27, 2024): e2037. http://dx.doi.org/10.7717/peerj-cs.2037.

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The rapid advancement of deepfake technology poses an escalating threat of misinformation and fraud enabled by manipulated media. Despite the risks, a comprehensive understanding of deepfake detection techniques has not materialized. This research tackles this knowledge gap by providing an up-to-date systematic survey of the digital forensic methods used to detect deepfakes. A rigorous methodology is followed, consolidating findings from recent publications on deepfake detection innovation. Prevalent datasets that underpin new techniques are analyzed. The effectiveness and limitations of estab

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Lin, Leo S. F. "Examining the Role of Deepfake Technology in Organized Fraud: Legal, Security, and Governance Challenges." Frontiers in Law 4 (April 16, 2025): 6–17. https://doi.org/10.6000/2817-2302.2025.04.02.

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Deepfake technology has evolved astonishingly by applying artificial intelligence (AI) to inspire ultra-realistic audio and video content. Initially praised for its legitimate use cases in entertainment and education, deepfake technology has increasingly become a tool for organized fraud and other malicious purposes. This paper investigates the role of deepfake technology in enabling identity theft, financial fraud, and unlawful activities. By conducting a qualitative comparative analysis of three cases, this paper analyzes deepfakes' legal, security, and governance aspects, indicating that de

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Shahzad, Hina Fatima, Furqan Rustam, Emmanuel Soriano Flores, Juan Luís Vidal Mazón, Isabel de la Torre Diez, and Imran Ashraf. "A Review of Image Processing Techniques for Deepfakes." Sensors 22, no. 12 (2022): 4556. http://dx.doi.org/10.3390/s22124556.

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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. Complement

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Prayas, Chaudhary, Jain Prasuk, Kumar Bhardwaj Rajnish, Tyagi Vasu, and Jalhotra Sonika. "Deepfake Video Face Detection using Deep Learning." Recent Trends in Information Technology and its Application 8, no. 3 (2025): 19–26. https://doi.org/10.5281/zenodo.15429570.

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<em>The proliferation of deepfake technology, which uses artificial intelligence to create highly realistic synthetic videos and images, poses major risks to privacy, security, and confidence in digital platforms. Traditional approaches to achieving these properties are often limited by the complexity of the algorithms. This paper proposes a novel approach for deepfake face detection using Deep Learning (DL) suited for sequential data analysis. Our method leverages the temporal dependencies and patterns inherent in video sequences to identify subtle inconsistencies and artifacts introduced by

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S. Praveena, R.Kaviya, K.Sheerin Farhana, and S.Bhuvanasri. "Deep Fake Video Detection Using Transfer Learning Resnet50." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 585–90. https://doi.org/10.47392/irjaem.2025.0094.

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The rapid development of deep learning technologies has enabled the creation of highly realistic deepfake videos, raising concerns in areas such as media integrity, privacy, and security. Detecting these deepfakes has become a significant challenge, as conventional methods struggle to keep pace with increasingly sophisticated techniques. This journal explores the application of transfer learning using ResNet50, a pre-trained convolutional neural network, for deepfake video detection. We present an overview of deepfake creation, the role of ResNet50 in transfer learning, the implementation proc

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A. Abu-Ein, Ashraf, Obaida M. Al-Hazaimeh, Alaa M. Dawood, and Andraws I. Swidan. "Analysis of the current state of deepfake techniques-creation and detection methods." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1659. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1659-1667.

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Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algo

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Ashraf, A. Abu-Ein1, M. Al-Hazaimeh2 Obaida, M. Dawood3 Alaa, and I. Swidan3 Andraws. "Analysis of the current state of deepfake techniques-creation and detection methods." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1659–67. https://doi.org/10.11591/ijeecs.v28.i3.pp1659-1667.

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Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algo

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Putri, Silvia Maharani Iskandar, Nashwa Salsabila, and Asmak UI Hosnah. "Kriminalisasi Penggunaan Deepfake dalam Tindak Pidana Penipuan dan Pencemaran Nama Baik: Tantangan dan Solusi Hukum." Jurnal Hukum Legalita 6, no. 2 (2024): 83–90. https://doi.org/10.47637/legalita.v6i2.1453.

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The increasing use of deepfake technology is causing new dangers in digital crimes like fraud and defamation. This technology can change audio-visual content in a way that makes it hard to tell apart from the original recording. This can allow criminals to use deepfakes for fraud or to harm someone's reputation without them or the public knowing. This journal analyzes how deepfakes are used for fraud and defamation in Indonesia. It looks for legal solutions to address these issues. This study used a normative legal research method to discover that the Electronic Information and Transaction Law

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Caci, Barbara, Giulia Giordano, Marianna Alesi, et al. "The public mental representations of deepfake technology: An in-depth qualitative exploration through Quora text data analysis." PLOS ONE 19, no. 12 (2024): e0313605. https://doi.org/10.1371/journal.pone.0313605.

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The advent of deepfake technology has raised significant concerns regarding its impact on individuals’ cognitive processes and beliefs, considering the pervasive relationships between technology and human cognition. This study delves into the psychological literature surrounding deepfakes, focusing on people’s public representation of this emerging technology and highlighting prevailing themes, opinions, and emotions. Under the media framing, the theoretical framework is crucial in shaping individuals’ cognitive schemas regarding technology. A qualitative method has been applied to unveil patt

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Garcia, Jan Mark. "Exploring Deepfakes and Effective Prevention Strategies: A Critical Review." Psychology and Education: A Multidisciplinary Journal 33, no. 1 (2025): 93–96. https://doi.org/10.70838/pemj.330107.

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Deepfake technology, powered by artificial intelligence and deep learning, has rapidly advanced, enabling the creation of highly realistic synthetic media. While it presents opportunities in entertainment and creative applications, deepfakes pose significant risks, including misinformation, identity fraud, and threats to privacy and national security. This study explores the evolution of deepfake technology, its implications, and current detection techniques. Existing methods for deepfake detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative

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Pallavi, Abburi. "DeepFake Detection for Human Face Images and Videos: A Comprehensive Survey." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47887.

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Abstract - With the growing sophistication of deep learning and generative models, the creation of synthetic media such as DeepFakes has become increasingly convincing and widespread. DeepFakes pose serious threats across multiple sectors, from political misinformation to personal identity theft. This paper reviews the current progress in DeepFake detection techniques focused on human facial images and video content. It categorizes detection methodologies into feature-based approaches, deep learning models, biological signal analysis, and multimodal systems. Additionally, it discusses benchmar

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Law Kian Seng, NORMAISHARAH MAMAT, Hafiza Abas, and Wan Noor Hamiza Wan Ali. "AI Integrity Solutions for Deepfake Identification and Prevention." Open International Journal of Informatics 12, no. 1 (2024): 35–46. http://dx.doi.org/10.11113/oiji2024.12n1.297.

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The increasing complexity of deepfake technology has sparked significant worries over individual privacy, the spread of false information, and deficiencies in cybersecurity. Deepfakes have the ability to effectively modify audio and visual content, resulting in a growing challenge to differentiate between real and fake content. To address this critical challenge, the study is conducting a survey to reveal a broad range of perspectives on the familiarity, encounters, and concerns related to deepfake technology. In addition, the study evaluates the effectiveness of current strategies in addressi

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Wildan, Jameel Hadi, Malallah Kadhem Suhad, and Rodhan Abbas Ayad. "A survey of deepfakes in terms of deep learning and multimedia forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4408–14. https://doi.org/10.11591/ijece.v12i4.pp4408-4414.

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Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfake detection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a d

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Patarlapati, Nagaraju. "Unmasking Reality: Exploring the Sociological Impacts of Deepfake Technology." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 882–89. http://dx.doi.org/10.22214/ijraset.2023.56639.

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Abstract: Deepfake technology, a rapidly advancing form of synthetic media, has gained prominence in recent years, raising profound concerns regarding its sociological impact. This study delves into the multifaceted repercussions of deepfakes within the realm of society. By employing a sociological lens, we aim to uncover the intricate ways in which deepfake technology influences individual behavior, interpersonal relationships, and societal norms. The research examines the origins and mechanics of deepfake technology, shedding light on its evolution from a niche hobby to a potent tool for man

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George, Abigail. "Defamation in the Time of Deepfakes." Columbia Journal of Gender and Law 45, no. 1 (2024): 122–72. https://doi.org/10.52214/cjgl.v45i1.13186.

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Deepfake technology, powered by artificial intelligence, has enabled the quick and easy creation of hyperrealistic videos that superimpose one person’s face onto another’s body. While the technology has benign applications, it has also been overwhelmingly used to create nonconsensual pornography. Deepfake pornography is a severe sexual offense that has targeted hundreds of thousands of women. This Note, the first comprehensive analysis of deepfake pornography under defamation law, sketches a framework for advocates and judges to apply defamation to cases of deepfake pornography. This Note argu

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Verma, Karishma. "Digital Deception: The Impact of Deepfakes on Privacy Rights." Lex Scientia Law Review 8, no. 2 (2024): 859–96. https://doi.org/10.15294/lslr.v8i2.13749.

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Deepfake technology, which uses advanced artificial intelligence to create synthetic media, poses significant threats to privacy rights. Since its emergence, deepfakes have been used in various malicious ways, raising urgent concerns about their impact on privacy rights. This study investigates the implications of deepfake technology on privacy, with a focus on how it affects individuals and legal frameworks. The research is driven by the need to understand the extent of privacy violations and the adequacy of current laws in addressing these challenges. The article aims to provide a comprehens

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Lee, Eun-Gi, Isack Lee, and Seok-Bong Yoo. "ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection." Mathematics 11, no. 18 (2023): 3952. http://dx.doi.org/10.3390/math11183952.

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Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques. This paper proposes a novel deepfake detection method to improve generalizability. We observe domain-wise independent clues in deepfake images, including inconsistencies in facial colors, detectable artifacts at synthesis boundaries, and disparities in quality between facial and nonfacial regions. This approach uses an interpatch dissimilarity estimator and a multistream c

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Broinowski, Anna. "Deepfake Nightmares, Synthetic Dreams: A Review of Dystopian and Utopian Discourses Around Deepfakes, and Why the Collapse of Reality May Not Be Imminent—Yet." Journal of Asia-Pacific Pop Culture 7, no. 1 (2022): 109–39. http://dx.doi.org/10.5325/jasiapacipopcult.7.1.0109.

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Abstract Since appearing in 2017, deepfakes have inspired a predominantly negative public response. Substantial research has been devoted to the danger that deepfake technology—as a deceptive audiovisual device—poses to democratic and evidentiary systems; and to the development of AI and legislative mechanisms to control it. However, the diverse and multiplying ways in which deepfake practitioners, researchers, and consumers are now viewing, framing, and using deepfake technology—and its positive applications in commerce, science, education, and the arts—deserve closer attention. This article

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Dobber, Tom, Nadia Metoui, Damian Trilling, Natali Helberger, and Claes de Vreese. "Do (Microtargeted) Deepfakes Have Real Effects on Political Attitudes?" International Journal of Press/Politics 26, no. 1 (2020): 69–91. http://dx.doi.org/10.1177/1940161220944364.

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Deepfakes are perceived as a powerful form of disinformation. Although many studies have focused on detecting deepfakes, few have measured their effects on political attitudes, and none have studied microtargeting techniques as an amplifier. We argue that microtargeting techniques can amplify the effects of deepfakes, by enabling malicious political actors to tailor deepfakes to susceptibilities of the receiver. In this study, we have constructed a political deepfake (video and audio), and study its effects on political attitudes in an online experiment ( N = 278). We find that attitudes towar

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AL-KHAZRAJI, Samer Hussain, Hassan Hadi SALEH, Adil Ibrahim KHALID, and Israa Adnan MISHKHAL. "Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications." Eurasia Proceedings of Science Technology Engineering and Mathematics 23 (October 16, 2023): 429–41. http://dx.doi.org/10.55549/epstem.1371792.

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Deepfake technology, which allows the manipulation and fabrication of audio, video, and images, has gained significant attention due to its potential to deceive and manipulate. As deepfakes proliferate on social media platforms, understanding their impact becomes crucial. This research investigates the detection, misinformation, and societal implications of deepfake technology on social media. Through a comprehensive literature review, the study examines the development and capabilities of deepfakes, existing detection techniques, and challenges in identifying them. The role of deepfakes in sp

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Татьяна Владимировна, Епифанова, and Копейкин Константин Игоревич. "Problems of legislative regulation of facilities created using deepfake technology in Russia and abroad." NORTH CAUCASUS LEGAL VESTNIK 1, no. 3 (2024): 121–28. http://dx.doi.org/10.22394/2074-7306-2024-1-3-121-128.

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The article attempts to consider the issues of legal regulation and protection of new objects that arise in the process of developing creativity on the Internet. The authors examine the legal regulation of objects created using deepfake technology in Russia and abroad and substantiate why deepfake, under certain conditions, can be classified as such an object of copyright as a work. They consider the best practices in regulating deepfake technologies in various countries that have achieved the greatest success in legal regulation of deepfakes (USA, China and Singapore). The main conclusion of

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Shilpa, K. C., B. P. Poornima, R. Pai Rajath, Harmain Khan Rakeen, N. S. Shreya, and A. Suchith. "A Comprehensive Review on Deep Fake Detection in Videos." Journal of Advancement in Architectures for Computer Vision 1, no. 1 (2025): 33–44. https://doi.org/10.5281/zenodo.15118896.

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<em>The last few decades have seen a significant rise in Artificial Intelligence (AI) and Machine Learning (ML), promoting the development of deepfake technology. Deepfakes are synthetic media created using AI techniques, altering audio, images, and videos to appear authentic but are fabricated. Employing concepts like Generative Adversarial Networks (GANs), deepfake creation involves a competitive process where one model produces forgeries while another aims to identify them. The consequences of deepfakes are extensive, ranging from misinformation campaigns by terrorist organizations to indiv

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Mrs. Sushma D. S, Sumanth T.C, Mehraj, Likhith.R, and Lohith T. R. "A Hybrid Approach to Deep Fake Detection Using Error Level Analysis." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 01 (2025): 98–102. https://doi.org/10.47392/irjaeh.2025.0013.

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The rapid advancement of ‘deepfake’ video technology— which uses deep learning artificial intelligence algorithms to create fake videos that look real—has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people’s alertness to and ability to detect a high-quality deepfake among a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compar

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Diljith, M. S., C. P. Emilyn, Afitha Abu T. Fathimathul, and KS Salkala. "Deepfake Technology: An Overview, Applications, Detection, and Future Challenges." Journal of Advancement in Architectures for Computer Vision 1, no. 1 (2025): 45–53. https://doi.org/10.5281/zenodo.15152176.

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<em>Deepfake technology, powered by artificial intelligence, has revolutionized digital media by enabling the creation of highly realistic synthetic videos, images, and audio. While it offers numerous benefits in fields such as entertainment, education, and accessibility, deepfake technology also raises significant ethical, legal, and security concerns. This report explores the methods used to generate deepfakes, including Generative Adversarial Networks (GANs) and autoencoders, and highlights key deepfake techniques such as face-swapping, lip-syncing, and voice cloning. It further examines bo

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Burgstaller, Markus, and Scott Macpherson. "Deepfakes in International Arbitration: How Should Tribunals Treat Video Evidence and Allegations of Technological Tampering?" Journal of World Investment & Trade 22, no. 5-6 (2021): 860–90. http://dx.doi.org/10.1163/22119000-12340232.

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Abstract Deepfakes can be described as videos of people doing and saying things that they have not done or said. Their potential use in international arbitration leads to two competing threats. Tribunals may be conscious of the difficulties in proving that a deepfake is, in fact, fake. If the ‘clear and convincing evidence’ standard of proof is applied, it may be very difficult, if not impossible, to prove that a sophisticated deepfake is fake. However, the burgeoning awareness of deepfakes may render tribunals less inclined to believe what they see on video even in circumstances in which the

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Abirami.R, Ms, Bharath G, and Jathin Viswa Sesha Sai G. "Identifying Social Media Deepfake Videos Using Deep Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43283.

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Deepfake videos are becoming more and more common on social media, endangering privacy, security, and the dissemination of accurate information. The project "Identifying Social Media Deepfake Videos Using Deep Learning Algorithms" attempts to address this issue. The system analyzes video frames using sophisticated deep learning methods, including Convolutional Neural Networks (CNNs), to identify minute alterations typical of deepfakes. The model is trained and evaluated using a carefully selected dataset of genuine and deepfake videos, guaranteeing reliable and accurate performance. To protect

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Akhtar, Zahid, Thanvi Lahari Pendyala, and Virinchi Sai Athmakuri. "Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve." Forensic Sciences 4, no. 3 (2024): 289–377. http://dx.doi.org/10.3390/forensicsci4030021.

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The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are extensively being harnessed across a diverse range of domains, e.g., forensic science, healthcare, virtual assistants, cybersecurity, and robotics. On the flip side, they can also be exploited for negative purposes, like producing authentic-looking fake news that propagates misinformation and diminishes public trust. Deepfakes pertain to audio or visual multimedia contents that have been artificially synthesized or digitally modified through the application of deep neural networks. Deepfakes can be em

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Kaan Tuysuz, Mustafa, and Ahmet Kılıç. "Analyzing the Legal and Ethical Considerations of Deepfake Technology." Interdisciplinary Studies in Society, Law, and Politics 2, no. 2 (2023): 4–10. http://dx.doi.org/10.61838/kman.isslp.2.2.2.

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This article aims to explore the multifaceted legal and ethical considerations of deepfake technology, with an emphasis on understanding its societal impact, regulatory challenges, and the ethical dilemmas it presents. The objective is to synthesize current academic insights into a comprehensive analysis that can inform both policy and practice in addressing the complexities introduced by deepfakes. A qualitative research design was utilized, employing semi-structured interviews with experts across fields relevant to deepfake technology, including technology law, digital ethics, multimedia tec

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Kumar K N, Mr Anil. "Fake Image Detection Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47455.

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Abstract — Design and implement a deepfake detection system capable of distinguishing authentic images from deepfake images that involve facial manipulation. This system should identify manipulated faces, thereby mitigating the harmful effects of deepfake technology. With the rapid advancement of image editing tools and generative technologies like deepfakes, the spread of manipulated or fake images has become a serious concern in areas ranging from social media to national security. Traditional methods of image verification are often inadequate due to the sophistication of modern forgeries.

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S, SARANYA. "Deepfake Detection using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46605.

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Abstract—Deepfake technology, driven by generative adversarial networks (GANs), poses significant challenges in digital security, misinformation, and privacy. Detecting deepfakes in images and videos requires advanced deep learning models. This study explores deepfake detection using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures like Vision Transformers (ViTs). We employ Meso4_DF deepfake detection pipeline that uses TensorFlow/Keras, PyTorch, OpenCV for processing, with Dlib, Scikit-Image, and NumPy for feature extraction, leveragi

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