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Bibliographic Details
Main Authors:	Real Safety AI Foundation, Gilly, Travis
Format:	Recurso digital
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Published:	Zenodo 2026
Subjects:	Threat analysis AI safety deepfakes perceptual consent image-based sexual abuse adversarial machine learning augmented reality AR wearables Threat modeling
Online Access:	https://doi.org/10.5281/zenodo.19242567
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Abstract In January 2026, we published a convergence threat analysis demonstrating that ambient non-consensual intimate image synthesis via AR wearables was architecturally feasible through cloud-assisted pipelines and would achieve edge-only feasibility within 12 to 24 months [1]. This technical update reports that multiple independent developments in the eight weeks following publication have compressed our timeline estimates significantly. Specifically, we identify three capabilities released between February and March 2026 that collectively reduce barriers across the critical pipeline stages: person segmentation (MatAnyone2, 140MB model achieving state-of-the-art video matting), inference acceleration (Diagonal Distillation, achieving 270x speedup over baseline video generation), and mobile 3D rendering (MobileGS, achieving 120+ FPS Gaussian splatting on a Snapdragon 8 Gen 3 phone at 4.8MB model size). We revise our feasibility classes accordingly, noting that the "edge full video synthesis" class we originally projected at 12 months may now be achievable within 6 to 9 months under moderate assumptions. We discuss implications for the policy recommendations in our original analysis and argue that the structural mismatch between capability maturation and regulatory response has widened.

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