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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 · 926 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

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  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

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  5. Deepfake Video Detection through Optical Flow Based CNN

    Irene Amerini, Leonardo Galteri, Roberto Caldelli, et al. · 2019 · 408 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

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  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 · 327 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

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

    Asad Malik, Minoru Kuribayashi, Sani M. Abdullahi, et al. · 2022 · IEEE Access · 283 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

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  8. A Survey on Deepfake Video Detection

    Peipeng Yu, Zhihua Xia, Jianwei Fei, et al. · 2021 · IET Biometrics · 244 citations

    Abstract Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism. So far, there have been a large amount of deepfake videos circulating on the Internet, most of which target at celebrities or politicians. These videos are often used to damage the reputation of celebrities and guide public opinion, greatly threatening social stability. Although the deepfake algorithm itself has no attributes of good or evil, this technology has been widely used for negative purposes. To prevent it from threatening human society, a series of research have been launched, including develo

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  9. A Novel Deep Learning Approach for Deepfake Image Detection

    Ali Raza, Kashif Munir, Mubarak Almutairi · 2022 · Applied Sciences · 210 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

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  10. Deepfake video detection: challenges and opportunities

    Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna, et al. · 2024 · Artificial Intelligence Review · 193 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

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  11. SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model

    Zhenglin Huang, Jinwei Hu, Xiangtai Li, et al. · 2024 · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 162 citations

    The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in digital content, resulting in severe repercussions. Despite some progress, academia has not yet created a large and diversified deepfake detection dataset for social media, nor has it devised an effective solution to address this issue. In this paper, we introduce the Social media Image Detection dataSet (SID-Set), which offers three key advantages: (1) extensive volume, featuring 300K AI-generated/tampered and authentic images with comprehensive

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  12. Deepfake Media Forensics: Status and Future Challenges

    Irene Amerini, Mauro Barni, Sebastiano Battiato, et al. · 2025 · Journal of Imaging · 98 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

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  13. A Robust Approach to Multimodal Deepfake Detection

    Davide Salvi, Honggu Liu, Sara Mandelli, et al. · 2023 · Journal of Imaging · 88 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

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  14. The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media

    Claire R. Leibowicz, Sean McGregor, Aviv Ovadya · 2021 · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · 33 citations

    Synthetic media detection technologies label media as either synthetic or non-synthetic and are increasingly used by journalists, web platforms, and the general public to identify misinformation and other forms of problematic content. As both well-resourced organizations and the non-technical general public generate more sophisticated synthetic media, the capacity for purveyors of problematic content to adapt induces a detection dilemma : as detection practices become more accessible, they become more easily circumvented. This paper describes how a multistakeholder cohort from academia, technology platforms, media entities, and civil society organizations active in synthetic media detection

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  15. Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems

    Naseem Khan, T. Nguyen, Amine Bermak, et al. · 2025 · ArXiv · 11 citations

    The rapid advancement of Generative Artificial Intelligence has fueled deepfake proliferation-synthetic media encompassing fully generated content and subtly edited authentic material-posing challenges to digital security, misinformation mitigation, and identity preservation. This systematic review evaluates state-of-the-art deepfake detection methodologies, emphasizing reproducible implementations for transparency and validation. We delineate two core paradigms: (1) detection of fully synthetic media leveraging statistical anomalies and hierarchical feature extraction, and (2) localization of manipulated regions within authentic content employing multi-modal cues such as visual artifacts an

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  16. 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

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

    Orkun Çınar, Yunus Doğan · 2025 · Applied Sciences · 10 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

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

    Farhan Ali, Zainab Ghazanfar · 2025 · ICCK Transactions on Intelligent Systematics · 6 citations

    The proliferation of deepfake technology poses significant threats to digital media authenticity, necessitating robust 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 spatial depende

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  20. Towards Explainable and Robust Deepfake Detection and Attribution: Enhancing Multimedia Forensics for the Next Generation of Synthetic Media

    Raphael Antonius Frick · 2025 · Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security · 1 citations

    The rise of generative AI has enabled the creation of synthetic audio, images, and videos that are virtually indistinguishable from authentic media, presenting new threats to digital trust, privacy, and security. While deepfake detection has advanced, most solutions focus on binary classification performed by data-driven approaches, which are insufficient for attribution and explainability required in high-stakes scenarios. This dissertation aims to develop robust, generalizable, and explainable forensic frameworks that (1) not only detect AI-generated media but also attribute attacks to specific models or methods, (2) provide interpretable evidence for forensic and legal contexts, and (3) a

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  21. Exploring Deepfake Image Forensics: The Role of CNNs, RNNs, GANs and Vision Transformers in Synthetic Media Detection

    Vishal V D, S. G, B. Sundarambal, et al. · 2025 · 2025 International Conference on Data Science and Business Systems (ICDSBS) · 1 citations

    The advancement of deepfake technology, which is the ability to produce synthetic media of convincingly real likenesses, has created enormous hurdles in the verification and security of digital content. In this light, several deep learning techniques have been employed in identifying deepfake content and lessening its effects. The paper looks at the most recent progress made in deepfake image forensics, especially Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), and Vision Transformers (ViTs). Models based on these CNN, particularly VGG16, VGG19 and ResNet50, are quite useful in the detection of deepfakes by spatial analysis feature

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  22. Deepfake Detection System Integrating CNN in GAN for Social Media Platforms

    C. Savithri, Manjushree E, J. E, et al. · 2025 · 2025 International Conference on Computing and Communication Technologies (ICCCT) · 1 citations

    Deepfake technology has seen rapid growth which has raised significant concerns regarding privacy, security, and digital trust. Reports from 2023 indicate that over 500,000 deepfake videos and voice manipulations were identified, reflecting a 550% increase since 2019, with fraud cases surging tenfold between 2022 and 2023, emphasizing the urgent need for robust detection mechanisms. This paper introduces a hybrid detection framework that integrates Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) in a parallel processing setup. The CNN component extracts deep features, identifying pixel-level inconsistencies and texture irregularities, while GAN-based adversari

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  23. Deepfake Detection in the Age of Synthetic Media: A Systematic Review

    Satyareddy Ogireddy, Gauri Mathur · 2025 · 2025 International Conference on Networks and Cryptology (NETCRYPT)

    In the current generation social media is the main form of communication for people around the globe. But it is useful in a positive manner. Also, it has some negative impacts such as the spreading of fake news by editing images and videos and sharing them on social media. This is the main area the entire world is looking at because of Artificial intelligence it has become much easier than before to create fake images and videos by using this. This creation of fake images or videos is termed Deepfake. These are the major threats to celebrities and politicians by changing the images and the audio of their videos and by spreading fake news and making people panic. To detect deepfakes many mach

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  24. From Deepfake Detection Research to Public Policy: A Survey on Generalization Challenges in Synthetic Media for the Brazilian Electoral Context

    Thauan de Souza Tavares Da Silva, D. A. Gonçalves, David Menotti · 2026 · Journal on Interactive Systems

    Deepfake detection has advanced rapidly in recent years, yet its practical effectiveness remains limited when models are exposed to real-world conditions that differ from controlled benchmarks. This gap is particularly critical in high-stakes scenarios such as electoral processes, where synthetic media can influence public perception at scale. This paper investigates the generalization limitations of current deepfake detection approaches, with emphasis on their applicability in the Brazilian context. Rather than focusing solely on algorithmic performance, we frame deepfake detection as a socio-technical problem, where issues of trust, interpretability, and user response are central to system

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