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Zenodo
2026
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| Online adgang: | https://doi.org/10.5281/zenodo.19242567 |
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| _version_ | 1866902083554246656 |
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| author | Real Safety AI Foundation Gilly, Travis |
| author_facet | Real Safety AI Foundation Gilly, Travis |
| contents | <p><strong>Abstract</strong></p> <p>In January 2026, we published a convergence threat analysis demonstrating that ambient<br>non-consensual intimate image synthesis via AR wearables was architecturally feasible through<br>cloud-assisted pipelines and would achieve edge-only feasibility within 12 to 24 months [1]. This<br>technical update reports that multiple independent developments in the eight weeks following<br>publication have compressed our timeline estimates significantly. Specifically, we identify three<br>capabilities released between February and March 2026 that collectively reduce barriers across the<br>critical pipeline stages: person segmentation (MatAnyone2, 140MB model achieving state-of-the-art<br>video matting), inference acceleration (Diagonal Distillation, achieving 270x speedup over baseline<br>video generation), and mobile 3D rendering (MobileGS, achieving 120+ FPS Gaussian splatting on a<br>Snapdragon 8 Gen 3 phone at 4.8MB model size). We revise our feasibility classes accordingly, noting<br>that the "edge full video synthesis" class we originally projected at 12 months may now be achievable<br>within 6 to 9 months under moderate assumptions. We discuss implications for the policy<br>recommendations in our original analysis and argue that the structural mismatch between capability<br>maturation and regulatory response has widened.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19242567 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Compressed Feasibility: A Technical Update to the Ambient Non-Consensual Synthesis Threat Model Real Safety AI Foundation Gilly, Travis Threat analysis AI safety deepfakes perceptual consent image-based sexual abuse adversarial machine learning augmented reality AR wearables Threat modeling <p><strong>Abstract</strong></p> <p>In January 2026, we published a convergence threat analysis demonstrating that ambient<br>non-consensual intimate image synthesis via AR wearables was architecturally feasible through<br>cloud-assisted pipelines and would achieve edge-only feasibility within 12 to 24 months [1]. This<br>technical update reports that multiple independent developments in the eight weeks following<br>publication have compressed our timeline estimates significantly. Specifically, we identify three<br>capabilities released between February and March 2026 that collectively reduce barriers across the<br>critical pipeline stages: person segmentation (MatAnyone2, 140MB model achieving state-of-the-art<br>video matting), inference acceleration (Diagonal Distillation, achieving 270x speedup over baseline<br>video generation), and mobile 3D rendering (MobileGS, achieving 120+ FPS Gaussian splatting on a<br>Snapdragon 8 Gen 3 phone at 4.8MB model size). We revise our feasibility classes accordingly, noting<br>that the "edge full video synthesis" class we originally projected at 12 months may now be achievable<br>within 6 to 9 months under moderate assumptions. We discuss implications for the policy<br>recommendations in our original analysis and argue that the structural mismatch between capability<br>maturation and regulatory response has widened.</p> |
| title | Compressed Feasibility: A Technical Update to the Ambient Non-Consensual Synthesis Threat Model |
| topic | Threat analysis AI safety deepfakes perceptual consent image-based sexual abuse adversarial machine learning augmented reality AR wearables Threat modeling |
| url | https://doi.org/10.5281/zenodo.19242567 |