AI RESEARCH
The Deployment Gap in AI Media Detection: Platform-Aware and Visually Constrained Adversarial Evaluation
arXiv CS.AI
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ArXi:2604.09706v1 Announce Type: cross Recent AI media detectors report near-perfect performance under clean laboratory evaluation, yet their robustness under realistic deployment conditions remains underexplored. In practice, AI-generated images are resized, compressed, re-encoded, and visually modified before being shared on online platforms. We argue that this creates a deployment gap between laboratory robustness and real-world reliability. In this work, we Our findings highlight that robustness measured under clean conditions substantially overestimates deployment robustness.