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Artykuły w czasopismach na temat „Deepfake Video Detection”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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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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Sameer, Sameer. "Integrating Deep Learning Architecture with Pufferfish Optimization Algorithm for Real-Time Deepfake Video Detection and Classification Model." Fusion: Practice and Applications 18, no. 1 (2025): 288–303. https://doi.org/10.54216/fpa.180120.

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Deepfake is a technology employed in making definite videos, which are operated utilizing an artificial intelligence (AI) model named deep learning (DL). Deepfake videos were normally videos that cover activities grabbed by definite people but with another individual's face. Substitute of people appearances in videos utilizing the DL model. The technology of Deepfake permits humans to operate videos and images utilizing DL. The outcomes from deepfakes are challenging to differentiate utilizing normal vision. It is a combination of the words DL and fake, and it mostly denotes material shaped by

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Dumbre, Aditya. "Deepfake Video Detection using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46035.

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Abstract - The rise of DeepFake (DF) technology poses a significant threat to the authenticity and reliability of digital media. While such videos once required expert skills and high-end software, the recent advancement of deep learning tools has made the generation of hyper-realistic synthetic media widely accessible. This paper presents a robust and efficient deep learning-based solution for detecting DeepFake videos by combining spatial and temporal analysis using a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN). Our final system

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Robert Wolański and Karol Jędrasiak. "Audio-Video Analysis Method of Public Speaking Videos to Detect Deepfake Threat." SAFETY & FIRE TECHNOLOGY 62, no. 2 (2023): 172–80. http://dx.doi.org/10.12845/sft.62.2.2023.10.

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Aim: The purpose of the article is to present the hypothesis that the use of discrepancies in audiovisual materials can significantly increase the effectiveness of detecting various types of deepfake and related threats. In order to verify this hypothesis, the authors proposed a new method that reveals inconsistencies in both multiple modalities simultaneously and within individual modalities separately, enabling them to effectively distinguish between authentic and altered public speaking videos. Project and methods: The proposed approach is to integrate audio and visual signals in a so-calle

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Lin, Chin-Yuan, Jen-Chun Lee, Shuenn-Jyi Wang, Chung-Shi Chiang, and Chao-Lung Chou. "Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity." Electronics 13, no. 11 (2024): 2132. http://dx.doi.org/10.3390/electronics13112132.

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With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed

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Tipper, Sarah, Hany F. Atlam, and Harjinder Singh Lallie. "An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection." Applied Sciences 14, no. 21 (2024): 9754. http://dx.doi.org/10.3390/app14219754.

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Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting

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Emaley, Aman Kumar. "Discerning Deception: A Face-Centric Deepfake Detection Approach with ResNeXt-50 and LSTMs." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5075–83. http://dx.doi.org/10.22214/ijraset.2024.61186.

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Abstract: A.I. has grown to epidemic proportions over the last years as its applied in almost all sectors to allocate workload from humans but end up being done effectively with no human intervention. A branch of A.I. called deep learning, which operates by mimicking human judgment and action through neural network systems. Nonetheless, with the increase height of the two platforms have been experienced sufficient cases of misguided individuals using tools to recycle videos, audios, and texts to achieve their agendas. This insinuates a due assumption that Generative Adversarial Networks, GANs,

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Sangeetha.V, Deepika Shri.N, and Sri Shivanuja.S. "SOLUTION FOR DETECTION OF FACE-SWAP BASED FAKE VIDEOS." international journal of engineering technology and management sciences 9, no. 2 (2025): 275–80. https://doi.org/10.46647/ijetms.2025.v09i02.035.

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Recent advancements in deep learning have dramatically simplified the creation ofhighly realistic deepfake (DF) videos, which were traditionally the domain of skilled visual effectsprofessionals. This surge in AI-synthesized media poses challenges in sectors such as politics,security, and entertainment. Detecting deepfakes is increasingly difficult due to the realism of thesevideos. In this paper, we propose a deepfake detection approach that combines ConvolutionalNeural Networks (CNNs) for extracting detailed frame-level features with Recurrent NeuralNetworks (RNNs) for modeling temporal inco

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Pankhuri, Tripathi, Singh Shikha, Nishad Anubhav, and Siddiqui Farheen. "Fraudshield – Deepfake Detection Tools." International Journal of Engineering and Management Research 15, no. 2 (2025): 47–51. https://doi.org/10.5281/zenodo.15314707.

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FraudShield is a web application designed to detect and mitigate the impact of deepfakes, ensuring content authenticity and integrity. With the rise of image manipulation and deepfake videos, detecting fraudulent activities has become increasingly critical. This project introduces a hybrid detection system that integrates Convolutional Neural Networks (CNNs) to identify morphed images and manipulated content. The framework leverages machine learning techniques to detect tampered facial features, artifacts, and inconsistencies in deepfake videos and images. The CNN component analyzes visual fea

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

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

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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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B. Shashi Varun Reddy, G. Ajay, M. Shiva Manideep, M. Sai Suraj, and L. Swathi Reddy. "Deepfake Video Detection Using LSTM Networks: A Temporal Sequence Learning Approach." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2215–20. https://doi.org/10.47392/irjaeh.2025.0325.

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With the swift emergence of deepfake videos, there is an urgent demand for sophisticated and effective detection methods to counter the dangers posed by misinformation and digital manipulation. This research examines the application of Long Short-Term Memory (LSTM) networks for the identification of deepfake content. LSTM, a variant of recurrent neural networks (RNNs), is recognized for its proficiency in learning time-related patterns in sequential data, rendering it particularly effective for analyzing the changing dynamics in video streams. The study aims to utilize LSTM architecture to ide

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Postiglione, Marco, Julian Baldwin, Natalia Denisenko, et al. "GODDS: The Global Online Deepfake Detection System." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29685–87. https://doi.org/10.1609/aaai.v39i28.35367.

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Fake audios, videos, and images are now proliferating widely. We developed GODDS, the Global Online Deepfake Detection system, for a specific user community, namely journalists. GODDS leverages an ensemble of deepfake detectors, along with a human in the loop, to provide a deepfake report on each submitted video/image/audio or VIA artifact submitted to the system. To date, VIA artifacts submitted by over 50 journalists from outlets such as the New York Times, Wall Street Journal, CNN, Agence France Press, and others have been run through GODDS. Unlike other deepfake detection systems, GODDS do

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Md, Fahimuzzman Sohan, Solaiman Md, and Aumit Hasan Md. "A survey on deepfake video detection datasets." A survey on deepfake video detection datasets 32, no. 2 (2023): 1168–76. https://doi.org/10.11591/ijeecs.v32.i2.pp1168-1176.

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Deepfake video has usefulness in entertainment and multimedia technology, however, the danger of deepfake is significant to the social, economical, and political sectors so far. Specifically, to diverge any public opinion by generating fake news and spreading misleading information, national security may be under risk due to misrepresenting statements given by political leaders. The creation of such manipulated videos are getting easier day by day and at the same time it is necessary to detect and prevent them. In order to do that, researchers are creating challenging fake video databases for

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Gupta, Gourav, Kiran Raja, Manish Gupta, Tony Jan, Scott Thompson Whiteside, and Mukesh Prasad. "A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods." Electronics 13, no. 1 (2023): 95. http://dx.doi.org/10.3390/electronics13010095.

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Recent advances in Generative Artificial Intelligence (AI) have increased the possibility of generating hyper-realistic DeepFake videos or images to cause serious harm to vulnerable children, individuals, and society at large with misinformation. To overcome this serious problem, many researchers have attempted to detect DeepFakes using advanced machine learning techniques and advanced fusion techniques. This paper presents a detailed review of past and present DeepFake detection methods with a particular focus on media-modality fusion and machine learning. This paper also provides detailed in

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López-Gil, Juan-Miguel, Rosa Gil, and Roberto García. "Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression." Computational Intelligence and Neuroscience 2022 (October 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/1332122.

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Recent technological advancements in Artificial Intelligence make it easy to create deepfakes and hyper-realistic videos, in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance and voice are used. As emotions are inherent in human communication, studying how deepfakes transfer emotional expressions from original to fakes is relevant. In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake datab

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

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complement

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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew

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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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Shimpi, A. N. "Deep Fake Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47392.

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ABSTRACT The rapid advancement of generative adversarial networks (GANs) and other AI-driven synthesis techniques, deepfake videos have emerged as a significant threat to digital media integrity, enabling the creation of highly realistic but fake video content. These manipulated videos can be used maliciously in disinformation campaigns, identity theft, and other cybercrimes, making their detection a critical challenge. This paper presents a deep learning-based approach for deepfake video detection that leverages both spatial artifacts and temporal inconsistencies introduced during the manipul

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Khormali, Aminollah, and Jiann-Shiun Yuan. "ADD: Attention-Based DeepFake Detection Approach." Big Data and Cognitive Computing 5, no. 4 (2021): 49. http://dx.doi.org/10.3390/bdcc5040049.

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Recent advancements of Generative Adversarial Networks (GANs) pose emerging yet serious privacy risks threatening digital media’s integrity and trustworthiness, specifically digital video, through synthesizing hyper-realistic images and videos, i.e., DeepFakes. The need for ascertaining the trustworthiness of digital media calls for automatic yet accurate DeepFake detection algorithms. This paper presents an attention-based DeepFake detection (ADD) method that exploits the fine-grained and spatial locality attributes of artificially synthesized videos for enhanced detection. ADD framework is c

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Tan, Lingfeng, Yunhong Wang, Junfu Wang, Liang Yang, Xunxun Chen, and Yuanfang Guo. "Deepfake Video Detection via Facial Action Dependencies Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5276–84. http://dx.doi.org/10.1609/aaai.v37i4.25658.

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Deepfake video detection has drawn significant attention from researchers due to the security issues induced by deepfake videos. Unfortunately, most of the existing deepfake detection approaches have not competently modeled the natural structures and movements of human faces. In this paper, we formulate the deepfake video detection problem into a graph classification task, and propose a novel paradigm named Facial Action Dependencies Estimation (FADE) for deepfake video detection. We propose a Multi-Dependency Graph Module (MDGM) to capture abundant dependencies among facial action units, and

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

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

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Cheritha, K., S. Akhil, V. Bhanu Prakash, and A. Akhil Reddy. "Deepfake Detection using Deep Learning with InceptionV3." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44058.

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Deepfake technology has rapidly evolved, making it increasingly difficult to distinguish between real and manipulated videos. This poses serious risks, including misinformation, identity theft, and digital forgery. To address this challenge, we propose a deep learning-based deepfake detection model that leverages InceptionResNetV2, a hybrid architecture combining the strengths of Inception networks and Residual networks (ResNet). Our approach efficiently extracts key facial features from video frames and classifies them as real or fake. The detection pipeline includes video preprocessing, fram

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Singh, Dr Viomesh, Bhavesh Agone, Aryan More, Aryan Mengawade, Atharva Deshmukh, and Atharva Badgujar. "SAVANA- A Robust Framework for Deepfake Video Detection and Hybrid Double Paraphrasing with Probabilistic Analysis Approach for AI Text Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2074–83. http://dx.doi.org/10.22214/ijraset.2024.65526.

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Abstract: As the generative AI has advanced with a great speed, the need to detect AI-generated content, including text and deepfake media, also increased. This research work proposes a hybrid detection method that includes double paraphrasing-based consistency checks, coupled with probabilistic content analysis through natural language processing and machine learning algorithms for text and advanced deepfake detection techniques for media. Our system hybridizes the double paraphrasing framework of SAVANA with probabilistic analysis toward high accuracy on AI-text detection in forms such as DO

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Wawan Kurniawan, Aliyah Kurniasih, and Muhamad Abdul Ghani. "Real or Deepfake Face Detection in Images and Video Data using YOLO11 Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1514–21. https://doi.org/10.59934/jaiea.v4i2.939.

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This study focuses on the detection of real and deepfake faces in images and video data using the YOLO11 algorithm. Deepfakes, generated using advanced deep learning techniques, have the potential for misuse, including spreading false information and identity theft. The research employs public datasets, namely EC2-DeepFake and Deepfake Dataset, to train a YOLO11-based model. The performance of the model is evaluated using metrics such as mAP50 and mAP50-95. Results indicate moderate accuracy in distinguishing real from fake faces, with notable challenges in handling diverse data. The findings

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P. Kamakshi Thai, Sathvik Kalige, Sai Nikhil Ediga, and Lokesh Chougoni. "A survey on deepfake detection through deep learning." World Journal of Advanced Research and Reviews 21, no. 3 (2023): 2214–17. http://dx.doi.org/10.30574/wjarr.2024.21.3.0946.

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Imagine watching a video where Tom Hanks delivers a rousing speech, but you suspect it might be fabricated. This growing concern stems from the rise of "DeepFakes," hyper realistic manipulated videos created using deep learning algorithms. These tools can seamlessly stitch together faces, voices, and body movements, blurring the lines between reality and fiction. While DeepFakes hold promise for entertainment and creative expression, their potential for misuse is significant. Malicious actors could leverage them to spread misinformation, damage reputations, or even influence elections. Thankfu

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P., Kamakshi Thai, Kalige Sathvik, Nikhil Ediga Sai, and Chougoni Lokesh. "A survey on deepfake detection through deep learning." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 2214–17. https://doi.org/10.5281/zenodo.14175452.

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Imagine watching a video where Tom Hanks delivers a rousing speech, but you suspect it might be fabricated. This growing concern stems from the rise of "DeepFakes," hyper realistic manipulated videos created using deep learning algorithms. These tools can seamlessly stitch together faces, voices, and body movements, blurring the lines between reality and fiction. While DeepFakes hold promise for entertainment and creative expression, their potential for misuse is significant. Malicious actors could leverage them to spread misinformation, damage reputations, or even influence elections. Thankfu

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Arsha, Anish, Shouckath Kolliyath Shebin, Joshy Sneha, K. Athira, and A. S. Revathy. "DEEP FAKE DETECTION USING MACHINE LEARNING." Research and Reviews: Advancement in Cyber Security 1, no. 3 (2024): 23–29. https://doi.org/10.5281/zenodo.13382382.

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<em>The recent proliferation of free, deep learning-based tools has democratized the creation of high-fidelity “deepfake” videos, where facial exchanges exhibit minimal manipulation artifacts. While advancements in visual effects have facilitated video manipulation for decades, deep learning has ushered in an era of unprecedented realism and accessibility for generating such “AI-synthesized media.” While crafting deepfakes is now relatively straightforward, their detection remains a significant challenge due to the complexities involved in training algorithms to recogni

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

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

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Sohail, Saud, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, and Muhammad Kazim. "Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning." Information 16, no. 4 (2025): 270. https://doi.org/10.3390/info16040270.

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This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. The background of the study emphasizes the growing threat of deepfakes, which pose significant challenges in various domains, including social media, politics, and entertainment. Current methodologies primarily rely on visual features that are specific to the dataset and fail to generalize well across varying manipulation techniques. However, these techniques focus on either spatial or te

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Salvi, Davide, Honggu Liu, Sara Mandelli, et al. "A Robust Approach to Multimodal Deepfake Detection." Journal of Imaging 9, no. 6 (2023): 122. http://dx.doi.org/10.3390/jimaging9060122.

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The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant situations, it is becoming crucial to distinguish between authentic and fake media. Nonetheless, though deepfake generation systems can create convincing images and audio, they may struggle to maintain consistency across different data modalities, such as producing a realistic video sequence where both visual frames and speech are fake and consistent

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Chaudhary, Uday. "Deepfake Detection Using Convolutional and Re-current Neural Network." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2572–75. https://doi.org/10.22214/ijraset.2025.68681.

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With the rise of deep learning technology, it is becoming easier to control audiovisual content, causing serious problems in terms of broadcast accuracy and security. Deepfakes, which are created using advanced machine learning techniques such as artificial intelligence networks (GANs), are increasingly used for disinformation, cybercrime, and smear purposes. This paper presents a deep learning-based approach to detect deepfake videos using physical and spatial features extracted from videos. We utilize a combination of convolutional neural networks and recurrent neural networks, specifically

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Nelson, Leema, Harshita Batra, and Radha P. "Deepfake Detection in Manipulated Images/ Audio/ Videos: A Three-Stage Multi-Modal Deep Learning Framework." Inteligencia Artificial 28, no. 76 (2025): 20–39. https://doi.org/10.4114/intartif.vol28iss76pp20-39.

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The proliferation of deepfake content presents a significant threat to digital integrity and necessitates the development of efficient detection techniques. This study aims to establish a three-stage framework utilizing advanced deep learning models for multimedia datasets encompassing audio, video, and image data. The initial stage comprises an XceptionNet-based image deepfake detection model developed by providing its capacity to capture subtle artifacts and inconsistencies through depth-wise separable convolutions. This model, developed using the CelebA dataset, achieved an accuracy of 95.5

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Alhaji, Hanan Saleh, Yuksel Celik, and Sanjay Goel. "An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning." Electronics 13, no. 12 (2024): 2398. http://dx.doi.org/10.3390/electronics13122398.

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The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal

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Amin, Muhammad Ahmad, Yongjian Hu, and Jiankun Hu. "Analyzing temporal coherence for deepfake video detection." Electronic Research Archive 32, no. 4 (2024): 2621–41. http://dx.doi.org/10.3934/era.2024119.

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<abstract><p>Current facial image manipulation techniques have caused public concerns while achieving impressive quality. However, these techniques are mostly bound to a single frame for synthesized videos and pay little attention to the most discriminatory temporal frequency artifacts between various frames. Detecting deepfake videos using temporal modeling still poses a challenge. To address this issue, we present a novel deepfake video detection framework in this paper that consists of two levels: temporal modeling and coherence analysis. At the first level, to fully capture tem

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Wang, Tianyi, and Kam Pui Chow. "Noise Based Deepfake Detection via Multi-Head Relative-Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14548–56. http://dx.doi.org/10.1609/aaai.v37i12.26701.

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Deepfake brings huge and potential negative impacts to our daily lives. As the real-life Deepfake videos circulated on the Internet become more authentic, most existing detection algorithms have failed since few visual differences can be observed between an authentic video and a Deepfake one. However, the forensic traces are always retained within the synthesized videos. In this study, we present a noise-based Deepfake detection model, NoiseDF for short, which focuses on the underlying forensic noise traces left behind the Deepfake videos. In particular, we enhance the RIDNet denoiser to extra

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

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

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Subha K, Benitlin. "Deepfake Video Detection Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48491.

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Abstract - In today’s digital landscape, the rapid advancement of deepfake technology has raised serious concerns due to its ability to generate highly convincing fake videos. These synthetic media artifacts are widely used to spread misinformation, manipulate public opinion, and perpetrate identity fraud, presenting significant challenges across social, political, and legal domains. Traditional detection methods often fall short when addressing the spatial and temporal anomalies introduced by deepfake algorithms. To combat this threat, we propose FakeSpotter, a hybrid deep learning framework

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