Relevant bibliographies by topics / Deepfake Video Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Deepfake Video Detection'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Deepfake Video Detection"

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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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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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Gosavi, Prof Amol. "Deepfake Video Face Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5840–47. https://doi.org/10.22214/ijraset.2025.69233.

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The emergence of deepfake technology, which relies on generative adversarial networks (GANs), has raised substantial concerns in the realm of digital media. This technology enables the manipulation of facial features in videos, leading to potential misuse for spreading false information, misrepresentation, and identity theft. As a result, there is a pressing need to establish robust methods for detecting deepfakes effectively. Detecting deepfake videos is particularly difficult due to their increasingly realistic appearance and the sophisticated techniques involved in their creation. This rese
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Deressa, Deressa Wodajo, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar, and Glenn Van Wallendael. "GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer." Applied Sciences 15, no. 12 (2025): 6622. https://doi.org/10.3390/app15126622.

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Deepfakes have raised significant concerns due to their potential to spread false information and compromise the integrity of digital media. Current deepfake detection models often struggle to generalize across a diverse range of deepfake generation techniques and video content. In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes an Autoencoder and Variational Autoencoder to learn from latent data distributions. By learning from the visual
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Mrs. Sushma D. S, Sumanth T.C, Mehraj, Likhith.R, and Lohith T. R. "A Hybrid Approach to Deep Fake Detection Using Error Level Analysis." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 01 (2025): 98–102. https://doi.org/10.47392/irjaeh.2025.0013.

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The rapid advancement of ‘deepfake’ video technology— which uses deep learning artificial intelligence algorithms to create fake videos that look real—has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people’s alertness to and ability to detect a high-quality deepfake among a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compar
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Prayas, Chaudhary, Jain Prasuk, Kumar Bhardwaj Rajnish, Tyagi Vasu, and Jalhotra Sonika. "Deepfake Video Face Detection using Deep Learning." Recent Trends in Information Technology and its Application 8, no. 3 (2025): 19–26. https://doi.org/10.5281/zenodo.15429570.

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<em>The proliferation of deepfake technology, which uses artificial intelligence to create highly realistic synthetic videos and images, poses major risks to privacy, security, and confidence in digital platforms. Traditional approaches to achieving these properties are often limited by the complexity of the algorithms. This paper proposes a novel approach for deepfake face detection using Deep Learning (DL) suited for sequential data analysis. Our method leverages the temporal dependencies and patterns inherent in video sequences to identify subtle inconsistencies and artifacts introduced by
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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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Pallavi, Abburi. "DeepFake Detection for Human Face Images and Videos: A Comprehensive Survey." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47887.

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Abstract - With the growing sophistication of deep learning and generative models, the creation of synthetic media such as DeepFakes has become increasingly convincing and widespread. DeepFakes pose serious threats across multiple sectors, from political misinformation to personal identity theft. This paper reviews the current progress in DeepFake detection techniques focused on human facial images and video content. It categorizes detection methodologies into feature-based approaches, deep learning models, biological signal analysis, and multimodal systems. Additionally, it discusses benchmar
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Noreen, Iram, Muhammad Shahid Muneer, and Saira Gillani. "Deepfake attack prevention using steganography GANs." PeerJ Computer Science 8 (October 20, 2022): e1125. http://dx.doi.org/10.7717/peerj-cs.1125.

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Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techn
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Ajith, Adithya. "A Review on Deepfake Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29735.

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Deepfake video detection is a new field in artificial intelligence (AI) and computer vision. Its main objective is to detect deepfake videos, which are digitally altered footage in which the original video is replaced with that of another person. "Deepfake video detection" is the process of recognizing and labelling videos that have been created by altering or substituting the appearance and actions of persons in the video through the use of deep learning techniques. These techniques are often used to create extremely realistic fake videos that can be used for deceptive purposes, such as sprea
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Dissertations / Theses on the topic "Deepfake Video Detection"

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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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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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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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Book chapters on the topic "Deepfake Video Detection"

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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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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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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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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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Ezeakunne, Uzoamaka, and Xiuwen Liu. "Facial Deepfake Detection Using Gaussian Processes." In Image and Video Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0376-0_27.

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Wang, Wenjie, Zhongyuan Wang, Guangcheng Wang, and Qin Zou. "Deepfake Video Detection Exploiting Binocular Synchronization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_9.

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Mahima, A. H., M. Monica, S. Neha, and Deepti Balaji Raykar. "DeepFake Image, Video and Audio Detection." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82383-1_3.

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Roy, Ritaban, Indu Joshi, Abhijit Das, and Antitza Dantcheva. "3D CNN Architectures and Attention Mechanisms for Deepfake Detection." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_10.

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AbstractManipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust det
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Gu, Yewei, Xianfeng Zhao, Chen Gong, and Xiaowei Yi. "Deepfake Video Detection Using Audio-Visual Consistency." In Digital Forensics and Watermarking. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69449-4_13.

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Li, Chanchan, Zuyi Song, Yutong Liang, and Xu’an Wang. "Survey on Technologies of Video Deepfake Detection." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86149-9_18.

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Conference papers on the topic "Deepfake Video Detection"

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Jakka, Aishwarya, Vakula Rani, Manoj Challa, M. Vinay Kumar, and Gopikrishnan Kookkal. "Deepfake Video Detection using Deep Learning Approach." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726218.

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Jha, Abhishek Kumar, Aman Kumar Yadav, Aman Kumar Dubey, Akash Kumar, and Ashish Sharma. "Deep Learning Based Deepfake Video Detection System." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986738.

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Kumar, Manoj, Praveen Kumar Rai, and Pankaj Kumar. "A Novel Approach for Deepfake Video Detection." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986564.

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Ciamarra, Andrea, Roberto Caldelli, and Alberto Del Bimbo. "Temporal surface frame anomalies for deepfake video detection." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00388.

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Wang, Yufei, and Guangjun Liao. "Deepfake Video Detection Based on Image Source Anomaly." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709022.

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Pitsun, Oleh, Nazar Melnyk, and Khrystyna Lipianina-Honcharenko. "Deepfake Detection Analysis Based on Video Face Analysis." In 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT). IEEE, 2024. https://doi.org/10.1109/csit65290.2024.10982588.

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Yeh, Jui-Feng, Ming-Jheng Shih, Jing-Xiang Yang, and Che-Kai Li. "Utilizing Recurrent Neural Network for Deepfake Video Detection." In 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, 2024. https://doi.org/10.1109/ecbios61468.2024.10885499.

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Kumar, Akash, Harikesh Singh, Akash Mishra, Ashish Kasaudhan, and Harshit Ralhan. "An Overview of DeepFake Video Detection and Mitigation." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986530.

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Wang, Xuefei, Guoqiang Zhong, and Qiang Song. "FakeFormer: Transformer-Based Lightweight Deepfake Video Detection Model." In 2025 7th International Conference on Information Science, Electrical and Automation Engineering (ISEAE). IEEE, 2025. https://doi.org/10.1109/iseae64934.2025.11042069.

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Sundaram, Vikram, Babitha Senthil, and Susmitha Vekkot. "Enhancing Deepfake Detection: Leveraging Deep Models for Video Authentication." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723844.

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