Murnitin: An Explainable, Adversarially Robust and Privacy-Preserving Academic Integrity Framework for the LLM Era

Current AI detectors like Turnitin act as black boxes, frequently mislabel non-native English writing and fail against simple evasion tricks while storing student papers in private databases. In this paper, we present Murnitin, an open framework that addresses these issues directly. We combine sentence-level explainable metrics (perplexity and burstiness) with real-time defense against zero-width and homoglyph attacks. Crucially, we introduce keystroke process verification to confirm authentic human drafting, which cuts the false positive rate on non-native English essays down to 2%. Finally, we implement zero-knowledge cryptographic hashing so institutions can verify document similarity without ever keeping raw student text on central servers.