Media Intelligence Platform for Detecting Manipulated Content

Paper: Media Intelligence Platform: Multimodal Detection of Synthetic and Manipulated Content

Authors: David Fischinger, Marcel Hasenbalg, Silvia Poletti, Davide Antonutti, Mina Sch"utz, Lam Pham, Justin Ilyes, P'eter Rjabcsenk'o, Simon Freitter, Martin Boyer

Executive Summary

Recent advancements in generative artificial intelligence have led to a surge in manipulated multimedia content, which includes synthetic faces, altered videos, AI-generated images, and fake audio. This increase raises concerns for businesses and organizations that rely on authentic media for trust and integrity in communications.

Current detection methods for identifying such manipulated content are often limited to specific types of media. For instance, a tool designed to detect fake videos may not be effective for spotting altered images or audio. This limitation can pose significant challenges in real-world situations where various types of media are used together.

To address these challenges, researchers have developed the Media Intelligence Platform (MIP), a comprehensive web-based framework for multimodal media forensics. MIP is designed to integrate various detection capabilities into a single platform. It includes specialized modules for identifying fake videos, detecting face morphing, recognizing AI-generated images, spotting audio deepfakes, and assessing localized image manipulations, along with tools for text analysis.

The modular architecture of MIP allows for flexible integration of different models, making it adaptable to new and emerging manipulation techniques. This adaptability is crucial as the landscape of multimedia manipulation continues to evolve.

In tests conducted using representative benchmark datasets, MIP demonstrated reliable detection performance across multiple types of media. These tests validate the platform's effectiveness in identifying manipulated content, which is crucial for ensuring authenticity in communications. The user-friendly interface of MIP enhances its practicality for forensic investigations, allowing users to navigate the system with ease.

Significantly, MIP is accompanied by a public demonstrator that supports reproducibility and community evaluation. This transparency is important for fostering trust in its capabilities and encourages further development in the field of media forensics.

Overall, the Media Intelligence Platform represents a promising step toward a unified solution for detecting deepfakes and other forms of manipulated media in diverse environments. Businesses could leverage such a tool to enhance their media verification processes, safeguard their reputation, and maintain trust with their customers.

Academic 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.