Exploring real-time verification gateways for AI-generated media to protect content authenticity and prevent deepfake spoofing.

As generative AI models become indistinguishable from reality, validating the origin and authenticity of digital media at network ingress is critical.

Abstract This thesis presents an edge-compatible **AI Verification Gateway** designed to inspect incoming image payloads for synthetic artifacts, provenance metadata signatures, and spectral anomalies.

Core Components - **Perceptual Feature Extraction**: Lightweight neural network heads running at low latency on edge nodes. - **Watermark & Provenance Inspection**: Cryptographic verification of C2PA metadata and latent watermarks. - **Risk Score Aggregation**: Dynamic scoring mechanism that routes high-risk media for human review or sandbox isolation.

Results Achieved sub-40ms latency overhead on high-throughput media ingestion pipelines while maintaining a 96.4% detection accuracy across synthetic diffusion outputs.