Media intelligence platform detects fake photos videos and audio
Media Intelligence Platform: Multimodal Detection of Synthetic and Manipulated Content
Computer Vision and Pattern RecognitionMultimedia
Summary
Synthetic and manipulated media like fake faces, videos, and audio are becoming more common as AI improves. The authors created the Media Intelligence Platform to detect many types of fake or altered media all in one place. This web-based system uses different specialized tools for spotting fake videos, AI-made images, altered audio, and more. Tests show it works reliably across various media types and helps investigators analyze suspicious content. It can also be expanded to handle new kinds of manipulations in the future.
What this means in practice
- •For media monitoring teams: Identify diverse manipulated media types from videos, images, and audio in one platform to streamline fake content detection processes.
- •For online content moderators: Use a unified system to detect various AI-generated and doctored media formats for faster review and removal of harmful fake content.
Authors
David Fischinger, Marcel Hasenbalg, Silvia Poletti, Davide Antonutti, Mina Schütz, Lam Pham, Justin Ilyes, Péter Rjabcsenkó, Simon Freitter, Martin Boyer
Abstract
The rapid progress of generative artificial intelligence has led to a significant increase in manipulated multimedia content, including synthetic faces, forged videos, AI-generated images, fake audio, and locally edited images. Existing detection approaches are typically designed for a single manipulation type, limiting their applicability in real-world scenarios involving heterogeneous media sources. In this work, we present the Media Intelligence Platform (MIP), a unified web-based framework for multimodal media forensics integrating specialized modules for fake video detection, face morphing detection, AI-generated image detection, audio deepfake detection, localized image manipulation detection, and text analysis. Its modular architecture enables flexible integration of heterogeneous models and supports future extensions for emerging manipulation techniques. Experimental results on representative benchmark datasets demonstrate reliable detection performance across multiple media modalities while providing a practical and user-friendly interface for forensic investigations. The platform highlights the potential of unified multimodal systems for comprehensive deepfake detection in real-world environments, and is accompanied by a public demonstrator released with this workshop submission to support reproducibility and community evaluation.