VeriGenAI 1.0: Ανίχνευση Συνθετικών Εικόνων και Επαλήθευση Ηλικίας

Περίληψη

The proliferation of generative AI poses a growing threat to digital identity systems, particularly in Know Your Customer (KYC) and underage verification workflows. This paper introduces VeriGenAI, a modular, privacy-first framework designed to detect AI-generated images in sensitive verification scenarios. Our system integrates facial landmark alignment, multimodal feature extraction, and transformer-based inference with counterfactual reconstruction using ComfyUI pipelines. Through an ensemble scoring model—combining GAN artifact detection, posture plausibility, age estimation, and metadata analysis—we achieve real-time judgments on image authenticity and demographic plausibility. The architecture supports air-gapped deployments and enables human-in-the-loop oversight. We further address challenges in latency, demographic bias, and adversarial robustness, offering transparent explainability features and forensic auditability. Beyond KYC and youth protection, VeriGen scales to applications in misinformation prevention, content moderation, and digital trust assurance. This work advances the frontier of trustworthy AI in identity validation through a secure, adaptable, and ethically grounded platform, while discusses the challenges of misusing such systems.