Relevant bibliographies by topics / Deepfakes

Contents

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

Academic literature on the topic 'Deepfakes'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "Deepfakes"

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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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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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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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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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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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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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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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Vinay, A., Paras S. Khurana, T. B. Sudarshan, et al. "AFMB-Net." Tehnički glasnik 16, no. 4 (2022): 503–8. http://dx.doi.org/10.31803/tg-20220403080215.

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With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deep
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Dissertations / Theses on the topic "Deepfakes"

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Wardh, Eric, and Victor Wirstam. "Deepfakes - En risk för samhället?" Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44904.

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En deepfake kan vara allt från en bild, video eller ljudklipp, manipulerad med hjälp av AI-teknologi. Deepfakes används legitimt i exempelvis spel- och filmindustrin, men det vanligaste användningsområdet för deepfakes är att skapa manipulerade bilder, videor eller ljudklipp för att sprida felaktig information. Ett annat användningsområde är för att få det att se ut som att personer som egentligen inte medverkat i den aktuella bilden, videon eller ljudklippet faktiskt har gjort det. Denna uppsats fokuserar på att undersöka hur deepfakes används och hur de kan användas för att påverka samhället
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Björklund, Christoffer. "Deepfakes inom social engineering och brottsutredningar." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42373.

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”Deepfake” är en förkortning av ”deep learning” och ”fake”. Deepfakes är syntetisk audiovisuell media som använder sig av maskininlärning för att generera falska videoklipp, bilder och/eller ljudklipp. Detta projekt fokuserar på deepfakes inom förfalskat videomaterial, där en persons ansikte i en video är utbytt mot en annan persons ansikte. Fokuset för den här rapporten är att undersöka hur enkelt det är att göra en egen deepfake med grundläggande kunskap. Detta är gjort med ett experiment som avser att mäta kvantitativa och kvalitativa resultat från intervjuer. Intervjuobjekten har tittat på
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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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Gardner, Angelica. "Stronger Together? An Ensemble of CNNs for Deepfakes Detection." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97643.

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Deepfakes technology is a face swap technique that enables anyone to replace faces in a video, with highly realistic results. Despite its usefulness, if used maliciously, this technique can have a significant impact on society, for instance, through the spreading of fake news or cyberbullying. This makes the ability of deepfakes detection a problem of utmost importance. In this paper, I tackle the problem of deepfakes detection by identifying deepfakes forgeries in video sequences. Inspired by the state-of-the-art, I study the ensembling of different machine learning solutions built on convolu
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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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Fontanella, Simone. "Rassegna degli strumenti per la creazione e l'individuazione di Deepfakes." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23156/.

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Questo volume di tesi descrive lo studio di una nuova tecnologia basata sull'intelligenza artificiale e l'apprendimento automatico nota con il nome di Deepfake. I deepfake nascono per manipolare il volto di una persona in una foto o in un video, attraverso dei modelli matematici noti come reti neurali artificiali. Il primo a fare uso di Deepfake fu un utente Reddit che voleva scambiare i volti delle attrici in video per adulti. I target di questa tecnologia sono spesso attori o personaggi famosi, visto la facilità e la quantità con cui possiamo reperire foto e video che li ritraggono. Sebbene
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Inevik, Mina. "Skenet bedrar: Fake porn i svensk straffrätt." Thesis, Uppsala universitet, Juridiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444204.

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Sedan år 2017 har många internetanvändare fått bekanta sig med fenomenet deepfakes som är en slags digital imitation där en persons ansikte kan fogas samman med en annan persons kropp. Så kallade face swap-appar och filter är inget nytt men det som särskiljer deepfakes är att de skapas med hjälp av artificiell intelligens som kan ge extremt verk- liga resultat. Dessutom blir tekniken allt mer avancerad och lättillgänglig för gemene man, vilket innebär större risk för att den missbrukas t.ex. för att skapa fake porn där en persons ansikte infogas i en existerande bild med sex
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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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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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Books on the topic "Deepfakes"

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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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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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Meikle, Graham. Deepfakes. Polity Press, 2022.

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Meikle, Graham. Deepfakes. Polity Press, 2022.

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Meikle, Graham. Deepfakes. Polity Press, 2022.

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Trouble with Deepfakes. Cherry Lake Publishing, 2024.

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Trouble with Deepfakes. Cherry Lake Publishing, 2024.

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Book chapters on the topic "Deepfakes"

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Gaur, Loveleen, and Gursimar Kaur Arora. "DeepFakes." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-7.

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Gaur, Loveleen, Jyoti Rana, and Amlan Chakrabarti. "DeepFakes." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-2.

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Raturi, Sonali, Amit Kumar Mishra, and Srabanti Maji. "Fake News Detection Using Machine Learning." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-10.

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Rastogi, Shreya, Amit Kumar Mishra, and Loveleen Gaur. "Detection of DeepFakes Using Local Features and Convolutional Neural Network." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-6.

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Gaur, Loveleen, Gursimar Kaur Arora, and Noor Zaman Jhanjhi. "Deep Learning Techniques for Creation of DeepFakes." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-3.

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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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Gaur, Loveleen, Mohan Bhandari, and Tanvi Razdan. "Development of Image Translating Model to Counter Adversarial Attacks." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-5.

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Mishra, Shubha, Piyush Kumar Shukla, and Ratish Agrawal. "DeepFakes, Media, and Societal Impacts." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-9.

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Sareen, Mamta. "Threats and Challenges by DeepFake Technology." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-8.

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Devi Vairamani, Ajantha. "Analyzing DeepFakes Videos by Face Warping Artifacts." In DeepFakes. CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-4.

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Conference papers on the topic "Deepfakes"

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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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Al-Sharawi, Ramzi, Hamza Athar, M. Riyyan Khan, et al. "Detecting Deepfakes with Super-Resolution EEG." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782476.

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Afroz, Kashifah, Swetha Chittam, and Kaushik Roy. "Understanding the Threat of Political Deepfakes." In 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC). IEEE, 2025. https://doi.org/10.1109/icaic63015.2025.10848832.

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Datta, Soumyya Kanti, Shan Jia, and Siwei Lyu. "Exposing Lip-syncing Deepfakes from Mouth Inconsistencies." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687902.

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Vandana and Kapil Chaturvedi. "Illusion or Reality: Analyzing Sentiments on Deepfakes." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689970.

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Rajeev, Faaizah Panapparambil, and Sujala Deepak Shetty. "Detecting Deepfakes and Artificial Intelligence-Generated Art." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864456.

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Yang, Chen, Bo Peng, Jing Dong, and Xiaoyu Zhang. "Unveiling Deepfakes with Latent Diffusion Counterfactual Explanations." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890239.

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Phatangare, Sheetal, Om Tekade, Prathamesh Thakare, Abhishek Wankhade, and Purva Wankhade. "PerceptiGuard: Unveiling DeepFakes Through Enhanced Media Perception." In 2024 IEEE Silchar Subsection Conference (SILCON). IEEE, 2024. https://doi.org/10.1109/silcon63976.2024.10910356.

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Stamnas, Sotirios, and Victor Sanchez. "DiffFake: Exposing Deepfakes Using Differential Anomaly Detection." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2025. https://doi.org/10.1109/wacvw65960.2025.00079.

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Pariki, Manohar, Srinivas Katakam, and Marella Srimathi. "An Experimental Analysis of Forensics and Deepfakes." In 2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom). IEEE, 2024. https://doi.org/10.1109/mosicom63082.2024.10881463.

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Reports on the topic "Deepfakes"

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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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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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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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Jacobs, Katrien. Deepfakes: can the AI be harnessed for something good? Edited by Shahirah Hamid and Michael Joiner. Monash University, 2024. http://dx.doi.org/10.54377/71fd-1709.

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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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