Real Safety AI Foundation / Research

Convergence Risk and Perceptual Modification

Compressed Feasibility: A Technical Update to the Ambient Non-Consensual Synthesis Threat Model

Publisher: Real Safety AI Foundation

Research paper.

Pages
6

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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Suggested citation

Gilly, Travis. "Compressed Feasibility: A Technical Update to the Ambient Non-Consensual Synthesis Threat Model." Real Safety AI Foundation Research Paper, n.d.. https://realsafetyai.org/research/compressed-feasibility/

References (6)

This list was read from the PDF text. Where the two differ, the PDF is correct.

  1. [1]T. Gilly, "Ambient Non-Consensual Image Synthesis: A Convergence Threat Analysis of AR Wearables, Real-Time Video Generation, and Open-Source Nudification Models," Zenodo, Jan. 2026. https://doi.org/10.5281/zenodo.18297286
  2. [2]P. Yang, S. Zhou, K. Hao, and Q. Tao, "MatAnyone 2: Scaling Video Matting via a Learned Quality Evaluator," in Proc. CVPR, 2026. arXiv:2512.11782. GitHub: https://github.com/pq-yang/MatAnyone2. HuggingFace Demo: https://huggingface.co/spaces/PeiqingYang/MatAnyone2.
  3. [3]J. Liu, X. Liu, K. Mei, Y. Wen, M.-H. Yang, and W. Liu, "Streaming Autoregressive Video Generation via Diagonal Distillation," in Proc. ICLR, 2026. arXiv:2603.09488. GitHub: https://github.com/Sphere-AI-Lab/diagdistill. Project page: https://spherelab.ai/diagdistill/.
  4. [4]X. Du et al., "Mobile-GS: Real-time Gaussian Splatting for Mobile Devices," in Proc. ICLR, 2026. arXiv:2603.11531. Project page: https://xiaobiaodu.github.io/mobile-gs-project/.
  5. [5]InSpatio-WorldFM: An Open-Source Real-Time Generative Frame Model for Spatial Intelligence. arXiv:2603.11911, March 2026.
  6. [6]T. Gilly, "Emergent Convergence Risk: Why Existing AI Governance Cannot Detect Combinatorial Threats and a Proposed Methodology," forthcoming, 2026.

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