iconic2409/hybrid-deepfake-detector

This is a proof-of-concept hobby project with zero adoption metrics (0 stars, 0 forks, 24 days old, no velocity). The README describes a straightforward ensemble of existing pre-trained deepfake detectors plus two custom models wrapped in a Streamlit interface—a standard pattern for ML demo applications. The technical approach (model ensembling, video frame analysis, confidence scoring) is commodity-level; deepfake detection itself is a well-established problem with numerous published benchmarks and commercial solutions (Facebook's DFDC, Microsoft Video Authenticator, etc.). The implementation appears to be a student/personal project combining off-the-shelf components without novel architectural or algorithmic contribution. Frontier labs (OpenAI, Anthropic, Google, Meta) are already shipping deepfake detection capabilities and synthetic media authentication tools; they would not see this as defensible IP. The Streamlit wrapper adds zero defensibility—it's a commodity UI framework. No evidence of novel training data, novel model architecture, or domain-specific insight. High frontier risk because deepfake detection is an active area for major labs, and they possess vastly superior datasets and compute.