Relevant bibliographies by topics / Deepfake Detection

Contents

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

Academic literature on the topic 'Deepfake Detection'

Author: Grafiati
Published: 25 January 2025
Last updated: 25 July 2025

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Journal articles on the topic "Deepfake Detection"

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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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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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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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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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&lt;p&gt;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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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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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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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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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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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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Dissertations / Theses on the topic "Deepfake Detection"

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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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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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Moufidi, Abderrazzaq. "Machine Learning-Based Multimodal integration for Short Utterance-Based Biometrics Identification and Engagement Detection." Electronic Thesis or Diss., Angers, 2024. http://www.theses.fr/2024ANGE0026.

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Le progrès rapide et la démocratisation de la technologie ont conduit à l’abondance des capteurs. Par conséquent, l’intégration de ces diverses modalités pourrait présenter un avantage considérable pour de nombreuses applications dans la vie réelle, telles que la reconnaissance biométrique ou la détection d’engagement des élèves. Dans le domaine de la multimodalité, les chercheurs ont établi des architectures variées de fusion, allant des approches de fusion précoce, hybride et tardive. Cependant, ces architectures peuvent avoir des limites en ce qui concerne des signaux temporels d’une durée
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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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Chang, Ching-Tang, and 張景棠. "Detecting Deepfake Videos with CNN and Image Partitioning." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394052%22.&searchmode=basic.

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碩士<br>國立中興大學<br>資訊科學與工程學系所<br>107<br>The AI­generated images are gradually similar to the pictures taken. When the generated images are used in inappropriate cases, it will cause damage to people’s rights and benefits. These doubtful images will cause illegal problems. The issue of detecting digital forgery has existed for many years. However, the fake images generated by the development of science and technology are more difficult to distinguish. Therefore, this thesis based on deep learning technology to detect the controversial face manipulation images. We proposed to segment the image bloc
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SONI, ANKIT. "DETECTING DEEPFAKES USING HYBRID CNN-RNN MODEL." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19168.

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We are living in the world of digital media and are connected to various types of digital media contents present in form of images and videos. Our lives are surrounded by digital contents and thus originality of content is very important. In the recent times, there is a huge emergence of deep learning-based tools that are used to create believable manipulated media known as Deepfakes. These are realistic fake media, that can cause threat to reputation, privacy and can even prove to be a serious threat to public security. These can even be used to create political distress, spread f
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RASOOL, AALE. "DETECTING DEEPFAKES WITH MULTI-MODEL NEURAL NETWORKS: A TRANSFER LEARNING APPROACH." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19993.

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The prevalence of deepfake technology has led to serious worries about the veracity and dependability of visual media. To reduce any harm brought on by the malicious use of this technology, it is essential to identify deepfakes. By using the Vision Transformer (ViT) model for classification and the InceptionResNetV2 architecture for feature extraction, we offer a novel approach to deepfake detection in this thesis. The highly discriminative features are extracted from the input photos using the InceptionResNetV2 network, which has been pre-trained on a substantial dataset. The Vi
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Books on the topic "Deepfake Detection"

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Abdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal, and Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.

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Abdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal, and Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.

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Abdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal, and Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.

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Abdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal, and Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.

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Gaur, Loveleen. Deepfakes: Creation, Detection, and Impact. CRC Press, 2022.

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Gaur, Loveleen. Deepfakes: Creation, Detection, and Impact. Taylor & Francis Group, 2022.

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Gaur, Loveleen. Deepfakes: Creation, Detection, and Impact. Taylor & Francis Group, 2022.

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Gaur, Loveleen. Deepfakes: Creation, Detection, and Impact. Taylor & Francis Group, 2022.

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Gaur, Loveleen. Deepfakes: Creation, Detection, and Impact. CRC Press LLC, 2022.

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Busch, Christoph, Christian Rathgeb, Ruben Vera-Rodriguez, and Ruben Tolosana. Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks. Springer International Publishing AG, 2021.

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Book chapters on the topic "Deepfake Detection"

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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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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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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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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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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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Saurav, Dheeraj Azad, Preeti Pandey, Mohammad Sheihan Javaid, and Utkarsh. "Deepfake Detection Using AI." In Advancement of Intelligent Computational Methods and Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003487906-19.

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Hernandez-Ortega, Javier, Ruben Tolosana, Julian Fierrez, and Aythami Morales. "DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_12.

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AbstractThis chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the recent fake detector named DeepFakesON-Phys that is based on a Convolutional Attention Network (CAN), which extracts spatial and temporal inform
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Nadimpalli, Aakash Varma, and Ajita Rattani. "GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37742-6_25.

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Nguyen, Y.-Hop, and Trung-Nghia Le. "Decoding Deepfakes: Caption Guided Learning for Robust Deepfake Detection." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4282-3_7.

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Rehman, Mariam, Mehran Rasool, and Sadaf Safder. "DeepFake Detection Using Deep Learning." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7969-1_11.

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Conference papers on the topic "Deepfake Detection"

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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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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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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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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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Wu, Hsiu-Fu, Chia-Yi Hsu, Chih-Hsun Lin, Chia-Mu Yu, and Chun-Ying Huang. "Deepfake Detection through Temporal Attention." In 2024 33rd Wireless and Optical Communications Conference (WOCC). IEEE, 2024. https://doi.org/10.1109/wocc61718.2024.10786063.

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Sarada, B., TVS Laxmi Sudha, Meghana Domakonda, and B. Vasantha. "Audio Deepfake Detection and Classification." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673438.

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S, Prakash Raj, Pravin D, Sabareeswaran G, Sanjith R. K, and Gomathi B. "Deepfake Detection Using Deep Learning." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717155.

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Ravale, Ujwala, Riya Ramesh Tattu, Ashish Baban Bhoir, and Sneha Bhaskar Mahajan. "Deepfake Detection using InceptionResNetV2 Model." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730917.

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Reports on the topic "Deepfake Detection"

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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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