PhD Proposal: Building Trust in GenAI: Robust Provenance, AI-Detection Limits, and Multimodal Reliability

As generative models advance across imagery and language applications, there is a growing need for mechanisms that establish content provenance, calibrate detection of AI-generated content, and ensure reliability that bear directly on the trustworthiness of GenAI among other factors.This work investigates robust provenance by proving a lower bound that exposes the vulnerability of imperceptible image watermarks under diffusion purification, demonstrating evasion and spoofing attacks in practice, and introducing interpretable and verifiable defenses (IConMark and DREW).For AI-content detection, we formalize a robustness–reliability trade-off and show that a universal, training-free adversarial paraphrasing procedure reliably degrades diverse deployed detectors, motivating calibrated operating regimes and explicit uncertainty communication.For multimodal reliability, we surface human-readable failure modes with PRIME, provide rigorous evaluation for text-guided image editing via EditVal, and improve compositionality with lightweight controls that make quality–fidelity trade-offs explicit.Together, these results connect theoretical limits with practical tooling to strengthen provenance, inform detection policy and practice, and expose and mitigate reliability failures in service of more trustworthy GenAI.