Opportunity: Neuro-Symbolic AI for Multimodal Content Analysis: Forensic Detection and High-Fidelity Video Retrieval

The modern information landscape presents a dual challenge for the Air Force: (1) the proliferation of sophisticated, multimodal AI-generated media (deepfakes) poses a significant threat to information integrity, and (2) the sheer volume of collected video and sensor data makes finding specific, mission-relevant events nearly impossible without exhaustive manual review. This research program addresses both challenges through a unified framework, leveraging Neuro-Symbolic AI to create systems that can not only forensically analyze multimodal content but also perform high-fidelity, prompt-based retrieval from massive video archives.
This topic seeks novel approaches that move beyond surface-level artifact detection. Instead, we aim to build systems that combine the pattern-recognition strengths of neural networks with the contextual reasoning of symbolic AI. This will enable a deeper understanding of relationships, objects, and actions within and across different media types (video, images, audio, text). The goal is to develop capabilities for both defending against AI-generated disinformation and for turning petabytes of raw footage into actionable, searchable intelligence.
Research areas of primary interest include:
• Advanced Multimodal Forensics & Red Teaming: This thrust focuses on the detection of AI-generated content that is explicitly designed to be undetectable. This includes developing novel generative techniques (e.g., via cross-modal diffusion models or GANs) to create stealthy synthetic media, which will then be used to red team and future-proof our detection systems. The goal is to build explainable forensic models that identify inconsistencies not just within a single modality, but across video, audio, and text domains.
• Neuro-Symbolic Video Analysis and Retrieval: This addresses the data overload problem by developing AI that can understand and respond to complex, natural-language prompts. The goal is to enable an analyst to retrieve a specific, short clip from hours of footage by asking a question like, “Show me all instances where a blue vehicle stops and a person exits carrying a package.” This requires the AI to build a structured, symbolic representation of the video’s content, enabling it to reason about objects, actions, and their temporal relationships without relying on simple keyword matching.
• Explainable and Verifiable AI: A critical component for both forensics and retrieval is trust. Any system developed must be able to explain why it flagged an image or video as synthetic or how it interpreted a prompt to find a specific result. This thrust will focus on creating verifiable models whose reasoning can be audited and understood by a human operator, ensuring that the AI is a trustworthy partner in the analytical process.