SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMs

Artificial Intelligence (AI) has made it possible for anyone to create images, audio, and video with unprecedented ease, enriching education, communication, and creative expression. At the same time, the rapid rise of AI-generated media has introduced serious risks, including misinformation, identity misuse, and the erosion of public trust as synthetic content becomes increasingly indistinguishable from real media. Although deepfake detection has advanced, many existing tools remain closed-source, limited in modality, or lacking transparency and educational value, making it difficult for users to understand how detection decisions are made. To address these gaps, we introduce SynthGuard, an open, user-friendly platform for detecting and analyzing AI-generated multimedia using both traditional detectors and multimodal large language models (MLLMs). SynthGuard provides explainable inference, unified image and audio support, and an interactive interface designed to make forensic analysis accessible to researchers, educators, and the public. The SynthGuard platform is available at: https://in-engr-nova.it.purdue.edu/

Key Contributions

Open, publicly accessible multi-modal detection platform supporting both AI-generated images and audio
First open MLLM-based explainable detection system that provides natural-language reasoning behind forensic detection outcomes

🛡️ Threat Analysis

Output Integrity Attack

Directly addresses AI-generated content detection — deepfake images and synthetic audio — which is the core of ML09's output integrity and content provenance scope. The novel contribution is integrating MLLMs to provide human-readable reasoning for why content is flagged as AI-generated, a new forensic analysis capability beyond simply deploying existing classifiers.