Relevant bibliographies by topics / Deepfake

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deepfake'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Deepfake"

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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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Dissertations / Theses on the topic "Deepfake"

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Wang, Xueyu. "DeepFake's Adversary: Disrupting DeepFake by Perturbations." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28642.

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In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would cause severe social impact and become a real threat to not only celebrities but also ordinary people. One way to defend the DeepFake is to disrupt the DeepFake generation by adding human-imperceptible perturbations to source inputs. Adding perturbations to the source inputs will make DeepFake results distorted from the perspective of human eyes. However, the ex
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Hasanaj, Enis, Albert Aveler, and William Söder. "Cooperative edge deepfake detection." Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.

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Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient wit
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Spinato, Claudia <1995&gt. "Arte e intelligenza artificiale nell'era dei deepfake." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/20119.

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Con lo sviluppo dell’intelligenza artificiale e dei nuovi media diventa sempre più difficile al giorno d’oggi orientarsi nel mondo digitale e della disinformazione. Il confine tra realtà e illusione è labile: i deepfake nell’ultimo decennio sono un pericolo sempre maggiore e spesso difficile da riconoscere. L’elaborato, basandosi sul concetto di iperrealismo secondo alcuni principali filosofi e affrontando il problema filosofico della distinzione tra immagine e realtà, ne segue lo sviluppo attraverso la nascita dei deepfake e dell’intelligenza artificiale (nello specifico delle GANs), individu
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Emir, Alkazhami. "Facial Identity Embeddings for Deepfake Detection in Videos." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170587.

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Forged videos of swapped faces, so-called deepfakes, have gained a  lot  of  attention in recent years. Methods for automated detection of this type of manipulation are also seeing rapid progress in their development. The purpose of this thesis work is to evaluate the possibility and effectiveness of using deep embeddings from facial recognition networks as base for detection of such deepfakes. In addition, the thesis aims to answer whether or not the identity embeddings contain information that can be used for detection while analyzed over time and if it is suitable to include information abo
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GUARNERA, LUCA. "Discovering Fingerprints for Deepfake Detection and Multimedia-Enhanced Forensic Investigations." Doctoral thesis, Università degli studi di Catania, 2021. http://hdl.handle.net/20.500.11769/539620.

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Forensic Science, which concerns the application of technical and scientific methods to justice, investigation and evidence discovery, has evolved over the years to the birth of several fields such as Multimedia Forensics, which involves the analysis of digital images, video and audio contents. Multimedia data was (and still is), altered using common editing tools such as Photoshop and GIMP. Rapid advances in Deep Learning have opened up the possibility of creating sophisticated algorithms capable of manipulating images, video and audio in a “simple” manner causing the emergence of a powerful
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Tak, Hemlata. "End-to-End Modeling for Speech Spoofing and Deepfake Detection." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS104.pdf.

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Les systèmes biométriques vocaux sont utilisés dans diverses applications pour une authentification sécurisée. Toutefois, ces systèmes sont vulnérables aux attaques par usurpation d'identité. Il est donc nécessaire de disposer de techniques de détection plus robustes. Cette thèse propose de nouvelles techniques de détection fiables et efficaces contre les attaques invisibles. La première contribution est un ensemble non linéaire de classificateurs de sous-bandes utilisant chacun un modèle de mélange gaussien. Des résultats compétitifs montrent que les modèles qui apprennent des indices discrim
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Weidenstolpe, Louise, and Jade Jönsson. "Manipulation i rörligt format - En studie kring deepfake video och dess påverkan." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20573.

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Med deepfake-teknologi kan det skapas manipulerade videor där det produceras falska bilder och ljud som framställs vara verkliga. Deepfake-teknologin förbättras ständigt och det kommer att bli svårare att upptäcka manipulerade videor online. Detta kan innebära att en stor del mediekonsumenter omedvetet exponeras för tekniken när de använder sociala medier. Studiens syfte är att undersöka unga vuxnas medvetenhet, synsätt och påverkan av deepfake videor. Detta eftersom deepfake-teknologin förbättras årligen och problemen med tekniken växer samt kan få negativa konsekvenser i framtiden om den utn
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Jönsson, Jade, and louise weidenstolpe. "Manipulation i rörligt format - En studie kring deepfake video och dess påverkan." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20776.

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Med deepfake-teknologi kan det skapas manipulerade videor där det produceras falska bilder och ljud som framställs vara verkliga. Deepfake-teknologin förbättras ständigt och det kommer att bli svårare att upptäcka manipulerade videor online. Detta kan innebära att en stor del mediekonsumenter omedvetet exponeras för tekniken när de använder sociala medier. Studiens syfte är att undersöka unga vuxnas medvetenhet, synsätt och påverkan av deepfake videor. Detta eftersom deepfake-teknologin förbättras årligen och problemen med tekniken växer samt kan få negativa konsekvenser i framtiden om den utn
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Firc, Anton. "Použitelnost Deepfakes v oblasti kybernetické bezpečnosti." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445534.

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Deepfake technológia je v poslednej dobe na vzostupe. Vzniká mnoho techník a nástrojov pre tvorbu deepfake médií a začínajú sa používať ako pre nezákonné tak aj pre prospešné činnosti. Nezákonné použitie vedie k výskumu techník pre detekciu deepfake médií a ich neustálemu zlepšovaniu, takisto ako k potrebe vzdelávať širokú verejnosť o nástrahách, ktoré táto technológia prináša. Jedna z málo preskúmaných oblastí škodlivého použitia je používanie deepfake pre oklamanie systémov hlasovej autentifikácie. Názory spoločnosti na vykonateľnosť takýchto útokov sa líšia, no existuje len málo vedeckých d
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Fjellström, Lisa. "The Contribution of Visual Explanations in Forensic Investigations of Deepfake Video : An Evaluation." Thesis, Umeå universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184671.

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Videos manipulated by machine learning have rapidly increased online in the past years. So called deepfakes can depict people who never participated in a video recording by transposing their faces onto others in it. This raises the concern of authenticity of media, which demand for higher performing detection methods in forensics. Introduction of AI detectors have been of interest, but is held back today by their lack of interpretability. The objective of this thesis was therefore to examine what the explainable AI method local interpretable model-agnostic explanations (LIME) could contribute
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Books on the topic "Deepfake"

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Taneja, Sanjay, Swati Gupta, Mohit Kukreti, and Ercan Ozen, eds. Navigating the Deepfake Conundrum: A Manager's Roadmap. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90742-5.

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Kalpokas, Ignas, and Julija Kalpokiene. Deepfakes. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93802-4.

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Gaur, Loveleen. DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493.

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Stuart, Anne. Deepfakes. CQ Press, 2024. http://dx.doi.org/10.4135/cqresrre20240816.

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Block, Martina. Deepfakes und Recht. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67427-7.

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Schuler, Alex. Deepfake. Level 4 Press, Inc., 2024.

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Deepfake. Scholastic, Incorporated, 2020.

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Littman, Sarah Darer. Deepfake. Scholastic, Incorporated, 2020.

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Schuler, Alex. Deepfake. Level 4 Press, Inc., 2021.

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Schuler, Alex. Deepfake. Level 4 Press, Inc., 2025.

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Book chapters on the topic "Deepfake"

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Li, Yuezun, Pu Sun, Honggang Qi, and Siwei Lyu. "Toward the Creation and Obstruction of DeepFakes." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_4.

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AbstractAI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5, 639 high-quality DeepFake videos of celebrities generated using an improved synthesis process. We conduct a comprehensive evaluati
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Durães, Dalila, Pedro Miguel Freitas, and Paulo Novais. "The Relevance of Deepfakes in the Administration of Criminal Justice." In Multidisciplinary Perspectives on Artificial Intelligence and the Law. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41264-6_19.

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AbstractNowadays, it is challenging to distinguish between genuine content created by humans or deepfake created by deepfakes algorithms. Therefore, it is in the interests of society and nations to have systems that can notice and evaluate the content without human intervention. This paper presents the challenges of artificial intelligence, specifically machine learning and deep learning, in the fight against deepfake. In addition, it presents the relevance that deepfakes may have in the administration of criminal justice.
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Korshunov, Pavel, and Sébastien Marcel. "The Threat of Deepfakes to Computer and Human Visions." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_5.

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AbstractDeepfake videos, where a person’s face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. The concern for the impact of the widespread deepfake videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes are for both human and computer visions by showing how well these videos can fool face recognition algorithms and naïve human subjects. We also show how well the state-of-the-art deepfake detection algorithms can detect deepfakes and whether they can outperform human
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Lyu, Siwei. "DeepFake Detection." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_12.

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AbstractOne particular disconcerting form of disinformation are the impersonating audios/videos backed by advanced AI technologies, in particular, deep neural networks (DNNs). These media forgeries are commonly known as the DeepFakes. The AI-based tools are making it easier and faster than ever to create compelling fakes that are challenging to spot. While there are interesting and creative applications of this technology, it can be weaponized to cause negative consequences. In this chapter, we survey the state-of-the-art DeepFake detection methods.
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Venema, Agnes E. "Deepfake Disinformation." In Routledge Handbook of Disinformation and National Security. Routledge, 2023. http://dx.doi.org/10.4324/9781003190363-16.

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Giansiracusa, Noah. "Deepfake Deception." In How Algorithms Create and Prevent Fake News. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7155-1_3.

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Dion, Goh Hoe-Lian, and Chei Sian Lee. "Deepfake Identification." In Fake News Across Asian Countries. Routledge, 2025. https://doi.org/10.4324/9781003403166-32.

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Gaur, Loveleen, Saurav Mallik, and Noor Zaman Jhanjhi. "Introduction to DeepFake Technologies." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-1.

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Bhilare, Omkar, Rahul Singh, Vedant Paranjape, Sravan Chittupalli, Shraddha Suratkar, and Faruk Kazi. "DEEPFAKE CLI: Accelerated Deepfake Detection Using FPGAs." In Parallel and Distributed Computing, Applications and Technologies. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29927-8_4.

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Hao, Hanxiang, Emily R. Bartusiak, David Güera, et al. "Deepfake Detection Using Multiple Data Modalities." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_11.

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AbstractFalsified media threatens key areas of our society, ranging from politics to journalism to economics. Simple and inexpensive tools available today enable easy, credible manipulations of multimedia assets. Some even utilize advanced artificial intelligence concepts to manipulate media, resulting in videos known as deepfakes. Social media platforms and their “echo chamber” effect propagate fabricated digital content at scale, sometimes with dire consequences in real-world situations. However, ensuring semantic consistency across falsified media assets of different modalities is still ver
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Conference papers on the topic "Deepfake"

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Tian, Ying, Wang Zhou, and Amin Ul Haq. "Detection of Deepfakes: Protecting Images and Vedios Against Deepfake." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873771.

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Dongre, Shital, Nilesh Hanamant Jadhav, Ravindra Jadhav, Sumedh Konkane, and Krishna Nilesh Jaiswal. "Enhanced deepfake detection through CNN and Deepfake Architecture." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911454.

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Sumathi, D., Ashu Singh, Arpita Sinha, D. Aditya, and Mohammed Riyaan K. F. "The Deepfake Dilemma: Enhancing Deepfake Detection with Vision Transformers." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915365.

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Ju, Yan, Chengzhe Sun, Shan Jia, et al. "DeepFake-o-meter v2.0: An Open Platform for DeepFake Detection." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00075.

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Munir, Sheza, Wassay Sajjad, Mukeet Raza, et al. "Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.861.

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Mishra, Sachin, Aakansha Sharma, Pushpendra Dhar Dwivedi, Prakhar Golchha, and Palak Lunia. "TransDFD: A Deepfake Detection System of Mesoscopic level Deepfake-guard-AI." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10984648.

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Krishnan, Anoop, and Amit Basu. "Towards Deepfake Detection for Everyone: A Lightweight Deepfake Detection Algorithm (LiDD)." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00293.

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Qin, Lixiong, Ning Jiang, Yang Zhang, et al. "Towards Interactive Deepfake Analysis." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888337.

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M, Chaitra, Kushal B, Likhitha H, and Priyanka H R. "AI-Powered DeepFake Defense." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915329.

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Chauhan, Surendra Singh, Arun Kumar Singh, Ashish Kumar Rastogi, Nitin Jain, Aman Kaushik, and Pramod Vishwakarma. "Deepfake Detection in Picture." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10956280.

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Reports on the topic "Deepfake"

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Busch, Ella, and Jacob Ware. The Weaponization of Deepfakes: Digital Deception on the Far-Right. ICCT, 2023. http://dx.doi.org/10.19165/2023.2.07.

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In an ever-evolving technological landscape, digital disinformation is on the rise, as are its political consequences. In this paper, we explore the creation and distribution of synthetic media by malign actors, specifically a form of artificial intelligence-machine learning (AI/ML) known as the deepfake. Individuals looking to incite political violence are increasingly turning to deepfakes–specifically deepfake video content–in order to create unrest, undermine trust in democratic institutions and authority figures, and elevate polarised political agendas. We present a new subset of individua
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Hwang, Tim. Deepfakes: A Grounded Threat Assessment. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20190030.

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The rise of deepfakes could enhance the effectiveness of disinformation efforts by states, political parties and adversarial actors. How rapidly is this technology advancing, and who in reality might adopt it for malicious ends? This report offers a comprehensive deepfake threat assessment grounded in the latest machine learning research on generative models.
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Azuaje Pirela, Michelle. “Deepfake Mom”: Desafíos tecnológicos y Derecho. Universidad Autónoma de Chile, 2021. http://dx.doi.org/10.32457/20.500.12728/90282021116.

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En el entendido de que estos videos (deepfake o ultrafalsos) suponen más de un problema que, como mínimo puede afectar los derechos de imagen, honor y reputación de las personas contenidas en ellos, la invitación es a preguntarnos si ¿estamos lo suficientemente preparados para la defensa de los mencionados derechos frente a ciertos usos de la inteligencia artificial
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Smith, Georgia. South Korea confronts a deepfake crisis. East Asia Forum, 2024. http://dx.doi.org/10.59425/eabc.1732010400.

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Azuaje Pirela, Michelle. Sobre el deepfake de Anthony Bourdain: ¿Así o más perturbador? Universidad Autónoma de Chile, 2021. http://dx.doi.org/10.32457/20.500.12728/90412021114.

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"Aunque jurídicamente, algunos ordenamientos (como el español, por ejemplo) incluyen disposiciones expresas que permiten el uso post mortem de la imagen (y, por tanto, de la voz) de alguien con autorización de sus herederos, cabe cuestionarse seriamente si en tiempos como los que vivimos y, con tecnologías como las que hoy tenemos, esto debería seguir siendo suficiente".
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Trauthig, Inga, Sebastián Valenzuela, and Philip N. Howard. Generative AI in Electoral Campaigns: Mapping Global Patterns. Edited by Kate Dommett and Dounia Mahlouly. International Panel on the Information Environment (IPIE), 2025. https://doi.org/10.61452/nvyo3144.

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This Summary for Policymakers provides a high-level précis of the Technical Paper, The Role of Generative AI Use in 2024 Elections Worldwide. GenAI is being deployed in many ways during elections, ranging from the creation of deepfake video and audio messages, to sophisticated voter targeting. What are the implications of GenAI for election administration and voter participation around the world? This assessment delivers the first global, data-driven analysis of its kind, designed to inform policy recommendations that enhance election administration, foster trust in electoral processes, and bo
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Trauthig, Inga, Sebastián Valenzuela, and Philip N. Howard. The Role of Generative AI Use in 2024 Elections Worldwide. Edited by Kate Dommett and Dounia Mahlouly. International Panel on the Information Environment (IPIE), 2025. https://doi.org/10.61452/hzue9853.

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A high-level précis of the Technical Paper can be found in the Summary for Policymakers report, Generative AI in Electoral Campaigns: Mapping Global Patterns. GenAI is being deployed in many ways during elections, ranging from the creation of deepfake video and audio messages, to sophisticated voter targeting. What are the implications of GenAI for election administration and voter participation around the world? This assessment delivers the first global, data-driven analysis of its kind, designed to inform policy recommendations that enhance election administration, foster trust in electoral
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Di Maio, P. Knowledge Representation To Identify, Expose and Prevent Deep Fakes. CSKRNS, 2020. http://dx.doi.org/10.52844/krdf.

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Deepfakes is a term used for resources which have been manipulated using new technology, in particular CNN [1]. This research presents the rationale for using KR (Knowledge Representation) mechanisms to identify, expose and prevent DeepFakes [1]https://doi.org/10.48550/arXiv.1905.00582
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Flynn, Asher, Anastasia Powell, Asia Eaton, and Adrian Scott. Legal loopholes don't help victims of sexualised deepfakes abuse. Edited by Shahirah Hamid and Chris Bartlett. Monash University, 2024. http://dx.doi.org/10.54377/02a7-d166.

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