[Synthetic] SFHQ-VirtualID, A Synthetic Face Dataset For Machine Unlearning: 750 Identities, 75,000 Portraits, 2 Releases With DOIs

I’ve just released SFHQ-VirtualID, a synthetic face dataset family built for identity-level machine unlearning. Everything is generated, so there are no real faces in it, and both releases have DOIs.

The problem I kept running into: in most face datasets used for unlearning, a person’s images are spread across splits, so “forgot the person” and “forgot some images” end up confounded. Here, each identity_id maps to exactly one split and contributes both train and holdout images (675 retain / 75 forget, 15-step protocol, MUFAC-aligned holdouts).

What ships:

– Bench: 67,500 balanced + 36,064 imbalanced 224×224 aligned crops. Uniform and seeded-Poisson forget schedules, plus a 5:1 long-tail popularity gradient for long-tail forgetting tests.

– Raw: 75,000 1024² portraits (100 per identity) with per-candidate prompt metadata for pose, expression, lighting, setting and camera.

I did not filter candidates on ArcFace identity similarity. The 0.40/0.45 thresholds are config defaults that ship as recorded columns (arcface_similarity, laplacian_variance, detection_confidence) rather than enforced filters, because silently dropping borderline-similar candidates hides exactly the confound that could explain a model’s apparent unlearning. Only the quality gate is enforced (detection plus Laplacian sharpness ≥ 80), and the 123 rejected candidates are documented in the manifest.

Reproducibility: 15-shard Slurm array on UoL’s Aire HPC (~120 GPU-hours), InstantID + Juggernaut-XL-v9 + ControlNet, pinned environment (torch 2.6/cu124, diffusers 0.39.0, insightface 1.0.1, pinned antelopev2 revision), RELEASE_MANIFEST.json and SHA-256 checksums.

Links:

Code · Hugging Face repos [Raw] [Bench] · Project Overview · Bench DOI 10.5281/zenodo.21877893 · Raw DOI 10.5281/zenodo.21879130

Caveats: the dataset is synthetic, so it’s a proxy for real-face benchmarks; demographic balance is inherited from the seed selection; and the two releases are related (Bench crops derive from the Raw candidates), so they aren’t independent test sets.

Happy to answer questions, and I’d genuinely like the split design stress-tested, since that’s the part I’d most want criticised.