Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis

This project identifies a genuine blind spot in the deepfake detection landscape: most models focus on lip-syncing and speech-related artifacts, leaving passive 'listening' states (reactions, micro-expressions while others speak) vulnerable. While the insight is clever, the defensibility is low (Score: 3) because it is currently a paper-based reference implementation with no community traction (0 stars). The technical moat in deepfake detection is notoriously shallow; once a new 'detection angle' is published, it is quickly integrated into broader ensembles by established players like Sentinel, Pindrop, or Reality Defender. Frontier risk is high because companies like Google, Meta, and Zoom have a vested interest in 'trusted video' and possess the massive datasets of real human interaction needed to train superior versions of this specific detection logic. The displacement horizon is short (1-2 years) because as generative models improve their temporal consistency (e.g., Sora, Kling), the specific 'listening' artifacts this project targets will likely evolve or disappear, requiring constant retraining.