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Research papers on Deepfake detection

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

    Orkun Çınar, Yunus Doğan · 2025 · Applied Sciences · 11 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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  2. 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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  3. The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media

    Claire Leibowicz, Sean McGregor, Aviv Ovadya · 2021 · arXiv (Cornell University) · 8 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 \newterm{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 d

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

    Farhan Ali, Zainab Ghazanfar · 2025 · ICCK Transactions on Intelligent Systematics · 8 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

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  5. Deepfakes and Synthetic Media: Generation, Detection, and Governance

    Alexandros Gazis, Efstathios Karypidis, Kleanthi Santamouri, et al. · 2026 · Encyclopedia · 1 citations

    Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaini

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  7. 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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  9. Identification of Deepfake Images and Synthetic Media for Forgery Detection

    Anand Kumar, Rakshita Gautam, Shipra Rohatgi, et al. · 2026 · IGI Global eBooks

    During past years, the easy accessibility of image editing tools such as Canva, FACEAPP, and CorelDRAW, many deepfake and synthetic images have been generated and circulated across media channels and the internet. Due to the emerging technologies such as Artificial Intelligence (AI) and Image Processing techniques, these tools show a capability of transforming human faces, swapping gender, and modifying other data of the images easily without any professional skills. The deepfake and synthetic images have raised challenges in multimedia and document forensics. Indeed, identifying these images through human eyes is difficult. The application of AI can tackle the problem. Various machine learn

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

    Satyareddy Ogireddy, Gauri Mathur · 2025

    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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  11. DeepFakeShield: AI-Based Detection of Deepfake Videos and Synthetic Media

    Dr.Rajesh Kumar P, S. Thanga Ramya, T Sam Paul, et al. · 2026 · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)

    The advancement of deep generative models has enabled the creation of highly realistic manipulated videos, commonly referred to as deepfakes, which pose unprecedented threats to digital security, media credibility, and public trust. As these synthetic media artifacts become more convincing, traditional detection methods are proving inadequate, creating an urgent need for advanced and explainable AI-driven approaches. With generative models producing increasingly realistic manipulations, conventional detection methods are no longer sufficient. To address this challenge, we introduce DeepFakeShield, an AI-based detection framework that integrates deep learning architectures with explainability

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  12. AI-Enabled Deepfake Forensics, Synthetic Identity Detection, and Media Authentication

    Murali Krishna Pasupuleti · 2026

    Abstract: AI-enabled deepfake forensics has emerged as a foundational discipline for protecting epistemic trust, digital identity, public communication, and institutional accountability in an era of synthetic media. This manuscript develops a doctoral-level framework for detecting, explaining, authenticating, governing, and operationalising forensic intelligence across images, audio, video, documents, behavioural traces, and platform-scale media streams. It progresses from foundations of uncertainty, research design, and evidentiary reasoning to statistical modelling, causal inference, machine-learning generalisation, scalable analytics, reproducible MLOps, sectoral applications, and governa

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  13. A Hybrid CNN-ANN Framework for Deepfake Detection and Synthetic Media Classification

    Dr. Ranjeet Kaur, Er. Mankirat Singh, Er. Deepinder Kaur, et al. · 2026 · Zenodo (CERN European Organization for Nuclear Research)

    The use of deepfake and synthetic media technologies has come a long way in the past few years, posing serious problems for the authentication of digital media and the prevention of misinformation. This work presents a novel Hybrid CNN–ANN approach to deepfake video detection, combining the advantages of deep spatial feature extraction and effective binary classification. The proposed method will use a sub-set of the DeepFake Detection Challenge (DFDC) dataset to train and test their models. First, input videos are sampled uniformly in time into temporal frames and then they are decoded. Denoising, CLAHE based contrast enhancement and image sharpening techniques are employed to extract and e

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  18. 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 SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

    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

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