Train now dedups by sha256 (earliest date wins), splits by hash group,
and drops post-cutoff rows whose hash was seen in training, so neither
holdout reports memorized duplicates. The feature schema is explicit
(v1 = the original 97 structural features, v2 adds CFA peaks, DCT AC
histograms and JPEG quant/Huffman/scan stats), stored in the bundle and
read back at scoring time; legacy bundles default to v1. Vectors are
fixed-width with NaN padding, so a sparse record no longer shifts every
column. Scoring runs in batches instead of one predict_proba per record.
Co-Authored-By: Claude <noreply@anthropic.com>
Corpus-mined vendor gaps (42k-file metadata scan, 2026-07-23):
- Bria AI signs C2PA as "Bria Artificial Intelligence" with source type
empty (no trainedAlgorithmicMedia), so identify was completely blind;
registered with asserts_ai like Dreamina.
- fal.ai ("fal - Features & Labels Inc.", fal-ai/<model> generators)
was detected via the source type but never attributed; registered,
also asserts_ai as a pure generative platform.
- Apple Photos Clean Up (Apple Intelligence object removal) was detected
as a generic made-with-AI tag; now attributed as an AI edit via the
photoshop:Credit marker, and the credit value joins
AI_GENERATOR_TOKENS so removal strips it in parity.
Each new test was verified red without its fix.
scripts/ai_score.py trains a gradient-boosted classifier on labels
derived from the scan_dataset metadata (C2PA AI generators, TC260,
local pipelines vs camera/screenshot/editor output) and scores every
file from pixel and container statistics alone, so metadata-stripped
files still get a score. Temporal holdout on the production corpus:
AUC 0.966 / AP 0.988. CLIP ViT-L/14 was evaluated as an alternative
and lost on both accuracy (0.82) and cost, so the model uses the
structural features the scanner already collects; scoring is CPU-only
and I/O-bound.
The invisible/SynthID diffusion pass loads the whole SDXL fp16 pipeline
into VRAM via `pipeline.to("cuda")`. On an 8 GB card the weights alone
(~7 GB) leave no room for activations, so the run OOMs and there is no
in-tool way to recover short of falling back to CPU (~9 min/image).
Add an opt-in `--cpu-offload` flag (default off) that calls diffusers'
`enable_model_cpu_offload()` instead: submodules are streamed to the GPU
on demand, dropping peak VRAM to roughly the largest single submodule at
the cost of per-step transfers. CUDA-only; a no-op on cpu/mps. Threaded
through `invisible`, `all`, and `batch` to keep the knob set identical
across the three, mirroring the existing `--device`/`--pipeline` options.
Measured on a GTX 1070 Ti (8 GB): `invisible --pipeline sdxl --cpu-offload`
runs the SynthID scrub on-GPU in ~2.5 min vs ~9 min on CPU, where the
default full-VRAM path OOMs.
Test drives the placement decision with a mock pipeline (no model/GPU),
gated on torch so it runs under the `gpu` extra and skips the core CI
matrix, consistent with the model-running test policy.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Route the pixel layer's base64 through the capped _b64 helper, replace
the pixels/pixels_full booleans with one pixel_mode parameter, record a
pixel skipped marker on oversized files, read EXIF from the already-read
bytes instead of re-reading the file, chunk the noise residual
convolution and replace the mgrid with 1D broadcast in the FFT features
(bit-identical values, ~300 MB less peak memory at 2048px), and fix the
raw-only docstring to account for the derived pixel layer.
Guard module-level numpy touchpoints so the metadata-only mode imports
and runs without numpy installed (deferred annotations, lazy DCT basis);
pixel modes now exit early with a clear numpy requirement message.
Victor Kuznetsov