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Research papers on Deepfake detection

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  2. Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News

    Cristian Vaccari, Andrew Chadwick · 2020 · Social Media + Society · 864 citations

    Artificial Intelligence (AI) now enables the mass creation of what have become known as “deepfakes”: synthetic videos that closely resemble real videos. Integrating theories about the power of visual communication and the role played by uncertainty in undermining trust in public discourse, we explain the likely contribution of deepfakes to online disinformation. Administering novel experimental treatments to a large representative sample of the United Kingdom population allowed us to compare people’s evaluations of deepfakes. We find that people are more likely to feel uncertain than to be misled by deepfakes, but this resulting uncertainty, in turn, reduces trust in news on social media. We

  3. DeepFakes: a New Threat to Face Recognition? Assessment and Detection

    Pavel Korshunov, Sébastien Marcel · 2018 · arXiv (Cornell University) · 497 citations

    It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto pornographic videos, call for automated ways to detect these Deepfake videos. To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. We used open source software based on GANs to create the Deepfakes, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. To demonstrate this impact

  5. Deepfake Video Detection through Optical Flow Based CNN

    Irene Amerini, Leonardo Galteri, Roberto Caldelli, et al. · 2019 · 402 citations

    Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video fram

  6. Deepfake detection using deep learning methods: A systematic and comprehensive review

    Arash Heidari, Nima Jafari Navimipour, Hasan Dağ, et al. · 2023 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 301 citations

    Abstract Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to

  7. DeepFake Detection for Human Face Images and Videos: A Survey

    Asad Malik, Minoru Kuribayashi, Sani M. Abdullahi, et al. · 2022 · IEEE Access · 265 citations

    Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural net

  8. A Novel Deep Learning Approach for Deepfake Image Detection

    Ali Raza, Kashif Munir, Mubarak Almutairi · 2022 · Applied Sciences · 202 citations

    Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Kingdom, United States, Canada, India, and South Korea. In 2019, cybercriminals generated fake audio c

  9. Deepfake video detection: challenges and opportunities

    Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna, et al. · 2024 · Artificial Intelligence Review · 176 citations

    Abstract Deepfake videos are a growing social issue. These videos are manipulated by artificial intelligence (AI) techniques (especially deep learning), an emerging societal issue. Malicious individuals misuse deepfake technologies to spread false information, such as fake images, videos, and audio. The development of convincing fake content threatens politics, security, and privacy. The majority of deepfake video detection methods are data-driven. This survey paper aims to thoroughly analyse deepfake video generation and detection. The paper’s main contribution is the classification of the many challenges encountered while detecting deepfake videos. The paper discusses data challenges such

  10. An Improved Dense CNN Architecture for Deepfake Image Detection

    Y. G. Patel, Sudeep Tanwar, Pronaya Bhattacharya, et al. · 2023 · IEEE Access · 173 citations

    Recent advancements in computer vision processing need potent tools to create realistic deepfakes. A generative adversarial network (GAN) can fake the captured media streams, such as images, audio, and video, and make them visually fit other environments. So, the dissemination of fake media streams creates havoc in social communities and can destroy the reputation of a person or a community. Moreover, it manipulates public sentiments and opinions toward the person or community. Recent studies have suggested using the convolutional neural network (CNN) as an effective tool to detect deepfakes in the network. But, most techniques cannot capture the inter-frame dissimilarities of the collected

  11. A Robust Approach to Multimodal Deepfake Detection

    Davide Salvi, Honggu Liu, Sara Mandelli, et al. · 2023 · Journal of Imaging · 82 citations

    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 one with the other. Moreover, these systems may not accurately reproduce semantic and timely accurat

  12. Deepfake Media Forensics: Status and Future Challenges

    Irene Amerini, Mauro Barni, Sebastiano Battiato, et al. · 2025 · Journal of Imaging · 79 citations

    The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like "Impostor Bias", a growing skepticism toward the authenticity of multimedia content, further complic

  13. Deepfake Satire and the Possibilities of Synthetic Media

    Joshua Glick · 2023 · Afterimage · 11 citations

    This article explores the rise of deepfake satire as one of the most vibrant subgenres of experimentation within the expanding field of AI art. A combination of “deep learning” and the word “fake,” deepfake videos depict people doing or saying things that they never did or said. While deepfakes have been used to deceive and harm individuals as well as a mass audience, they have also been used toward alternative ends. Deepfake satire offers artful social critique, interrogating the individuals and institutions it portrays as much as the technology used to create it and the platforms on which it circulates. Videos range from snarky jabs at entertainment industry personalities to hard-hitting t

  14. Novel Deepfake Image Detection with PV-ISM: Patch-Based Vision Transformer for Identifying Synthetic Media

    Orkun Çınar, Yunus Doğan · 2025 · Applied Sciences · 7 citations

    This study presents a novel approach to the increasingly important task of distinguishing AI-generated images from authentic photographs. The detection of such synthetic content is critical for combating deepfake misinformation and ensuring the authenticity of digital media in journalism, forensics, and online platforms. A custom-designed Vision Transformer (ViT) model, termed Patch-Based Vision Transformer for Identifying Synthetic Media (PV-ISM), is introduced. Its performance is benchmarked against innovative transfer learning methods using 60,000 authentic images from the CIFAKE dataset, which is derived from CIFAR-10, along with a corresponding collection of images generated using Stabl

  15. Enhanced Deepfake Detection Through Multi-Attention Mechanisms: A Comprehensive Framework for Synthetic Media Identification

    Farhan Ali, Zainab Ghazanfar · 2025 · ICCK Transactions on Intelligent Systematics · 4 citations

    The proliferation of deepfake technology poses significant threats to digital media authenticity, necessitating robust intelligent detection systems to combat manipulated content. This paper presents a novel attention-based framework for deepfake detection that systematically integrates multiple complementary attention mechanisms to enhance discriminative feature learning. Our approach combines spatial attention, multi-head self-attention, and channel attention modules with a VGG-16 backbone to capture comprehensive representations across different feature spaces. The spatial attention mechanism focuses on discriminative facial regions, while multi-head self-attention captures long-range spa

  19. Deepfake Media Detection Framework Using Machine Learning with Multimodal Feature Extraction for Real and Synthetic Content

    Shraddha Veer, Dr. Sudhir Mohod · 2026 · International Journal of Engineering and Creative Science

    Abstract—The emergence of contemporary deepfakes has at tracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content.Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, making it difficult to distinguish between real and fake features. This technology has greatly advanced, promoting a wide range of applications in cinema, such as improv

  20. Deepfake and Synthetic Media Detection

    Tejaswini Lokhande, Shreya Ghadge, Janhavi Lakeri, et al. · 2026 · International Research Journal on Advanced Engineering Hub (IRJAEH)

    Deepfake and synthetic media technologies have quickly changed with the growth of artificial intelligence. This has raised serious concerns about misinformation, security, and digital trust. This review paper looks at recent research on deepfake detection in image, video, and audio areas. It studies various methods like Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), MesoNet, and Multilayer Perceptron (MLP) models to see how well they identify manipulated content. The review points out that while existing methods show high accuracy on controlled datasets, they struggle in real-world situations, including issues like compression, noise, and unfamiliar manipulation

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