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Papers on “deepfake detection synthetic media”

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1. Deepfake Satire and the Possibilities of Synthetic Media

   Joshua Glick · 2023 · Afterimage · 11 cites

   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

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

   Orkun Çınar, Yunus Doğan · 2025 · Applied Sciences · 9 cites

   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

3. 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 cites

   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

4. Deepfake Detection and Multimedia Forensics: Investigating Synthetic Media, Image Forgery, and Video Manipulation in Cybercrime Cases

   Nishchal Soni · 2025 · ARC Journal of Forensic Science · 1 cite

5. Defend Against Deepfake Cyberattacks: A Scenario-Based Zero Trust Blueprint for Synthetic Media Resilience

   Thirupathaiah Peram · 2026 · International Journal of Science and Research (IJSR)

6. An Audiovisual Deepfake Detection Framework for Real and Synthetic Media Using Deep Learning

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

7. 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

8. 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