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.
--pixels adds aggregate, non-reconstructable pixel statistics (block-DCT
histograms with Benford deviation, high-pass noise stats, FFT band
energies and CFA peaks, ELA stats, gradient and color histograms) plus
per-section timing for pipeline latency planning. --pixels-full adds the
privacy-lifting artifacts behind an explicit flag (perceptual hash,
128px thumbnail, coarse ELA/noise-residual/FFT-phase maps). Expensive
maps are computed once and shared between the scalar and artifact paths
(2.2x speedup vs the first split implementation). numpy is required only
for the pixel modes; the metadata-only mode stays numpy-free.
Strip all derived output: the library verdict, the XMP edit-trail
parse, and the IJG quality estimate. The script now collects raw bytes
plus mechanical container decodes only, is fully standalone (no
remove_ai_watermarks import), and resumes interrupted runs. Add GIF,
BMP, and TIFF magic bytes to sniff_format (GIF files in the wild were
reported as unknown). Validated on the full 42k-file corpus: 100%
coverage, zero error records.
Single-file read-only scanner producing one JSONL record per image:
AI verdict via identify (metadata-only, no visible marks), full EXIF/
IPTC/XMP/PNG/JPEG/WebP/ISOBMFF inventories, C2PA store, JPEG encoder
forensics (quant tables, IJG quality, scan script, Huffman, subsampling),
XMP edit-trail fields, hashes/timestamps, macOS download provenance,
Live Photo pairing id. Runs standalone (pillow/piexif/c2pa-python) with
the library optional; supports gzip output, resume after interruption,
and head-only scanning of oversized files.
New engines, each calibrated on its TC260 USCC cohort and validated by a
full-corpus sweep (42009 files):
- runninghub: top-left corner (new corner="tl"), faint mid-gray text via
the new raw-grayscale "gray" detection front-end, anchor-position gate
- baidu: text-run-only template (pill is a bright-blob magnet), load-bearing
Doubao+Qwen rival margins, corner-extended footprint for the white tag
- liblib: bottom-center (new corner="bc"), Arial silhouette (font is the
discriminative lever against latin UI text), logo-extended footprint
Qingyan parked (no clean-arm separation at any render/box), MiniMax/Hailuo
parked (1 visible frame, the xinghui rule); silhouettes kept as starting
points.
Kling (USCC cohort 91110108335469089C, n=30): kling_engine.py, gate 0.35
(clean p99 0.304 / max 0.320), strict-only, unimodal 0.12/short on the
shared ladder, fitted locate box, no rival margin (crossfire 1/400 doubao
below gate, 0 jimeng, 0 clean), parity 9/9 detect->fill->re-detect.
Suppresses the jimeng pill like doubao/qwen. identify gains visible_kling.
Yuanbao: measured negative -- the two-line italic block does not separate
from clean corners on either front-end at any render/box/font setting;
the fitted recipe stays in render_vendor_silhouettes.py MARK_OPTS.
cat-logo: cohort has only 2 unique carriers, parked on evidence; the
draw_catlogo silhouette already separates (0.50 vs clean max 0.333), so
registration is a gate pick once more uniques arrive.
vendor_mark_calibrate: --fit-geometry takes locate-box overrides (two-line
marks were clipped by the inherited box) and the aspect sweep reaches 0.62.
Calibrated on the 117-frame TC260-producer cohort (vendor_cohort_harvest +
vendor_mark_calibrate, both committed here): per-mark 2-rung ladder
(0.78, 1.27) for the two measured size modes, fitted locate box (the mark
sits ~0.025 of the short side off the edge; doubao's box clipped the first
glyph), measured template aspect 0.26, gate 0.45 (clean p99 0.301).
Strict-only (the sub-gate band is non-Qwen banners), no rival margin
(0 cross-fires on 400 doubao / 298 jimeng / 286 clean frames).
83/83 real marks detector-clean after cv2 fill.
TextMarkConfig gains a per-mark ladder field; the shipped 3-rung default
is unchanged for every other mark.
The section had accumulated incremental edits: defects numbered out of order
(1, 2, 3a, 3, 5, 4), closed items mixed into the open table, and "what to do
next" spread across four subsections that partly repeated each other.
Now it opens with START HERE -- the prioritized next actions and the reason each
sits where it does -- followed by open defects only, renumbered 1-5. Closed items
move to their own subsection, keeping the faint-mask post-mortem because how it
escaped both parity and its own regression test is the instructive part.
Adds a table of the completed full runs with their artifacts and row counts, each
verified against the file on disk. That exists because the artifacts are the
answer to "did we actually cover X" -- relaunching a sweep costs hours and returns
nothing new, and this session nearly did exactly that before checking. It also
names the two fast confirmations (real_examples_e2e ~2 min, robustness_suite
~3 min) that a later change should run instead.
Adds invisible_engine.py:346 as an open defect: it still discards imwrite's
success flag, the same shape as the crash fixed in the previous commit.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The Tier E adversarial sweep (new, scripts/robustness_suite.py) drove the real CLI
over truncated, corrupt, zero-byte, absurdly-shaped and bomb inputs, unicode and
RTL paths, hostile output directories and concurrent runs. It found two crashes;
the /simplify review then reproduced a third and worse one.
1. A FAILED WRITE CRASHED ON THE SIZE REPORT. image_io.imwrite is contractually
non-raising and returns False, but write_bgr_with_alpha discarded that bool and
returned None, so no caller could tell a failed write from a successful one.
Every write site then ran output.stat() to print the size, so a read-only
destination died with a bare FileNotFoundError pointing at the stat rather than
the write. The fix is deliberately NOT uniform: single-image commands exit via
the new cli._write_output_or_exit; api._write_visible_result RAISES so a library
caller gets an accurate error instead of a confusing FileNotFoundError from the
downstream metadata strip; and the batch sites raise but never SystemExit,
because the batch loop counts per-image exceptions and aborting would kill the
whole run.
2. BATCH LOST DATA SILENTLY. Into a read-only output directory it wrote ZERO files
for 2 inputs and exited 0 -- no traceback, no error, an empty output directory a
wrapping service would read as a completed run. The robustness harness could not
see this class at all, since it scored exit codes and traceback markers and this
failure has neither; it now asserts on the artifacts written.
3. A DIRECTORY PASSED AS THE IMAGE crashed the metadata scanner with
IsADirectoryError, because click.Path(exists=True) accepts directories. Fixed
with dir_okay=False on all six source arguments, so argument parsing refuses it.
Also adds Tier B4 (scripts/resource_ceilings.py): peak RSS per fill backend from
1 MP to 25 MP, one fresh process per cell. migan 603->775 MB and lama 4679->4779 MB
are flat in input size, confirming the crop-around-the-mask design and both
documented figures; cv2 is the only backend that grows (74->440 MB, 5.9x). The
harness's own no-op check originally allocated a full-frame temp before reading
peak RSS and inflated the numbers with input size -- it now compares only the mask
box, and the conclusion survived re-measurement.
And scripts/real_examples_e2e.py, which drives every command over real corpus
examples and checks the outcome rather than the exit code: 6/6 provenance classes
identified, 10/10 metadata strips re-scan clean, all three fill backends write,
diffusion on MPS writes genuinely changed images. It records samsung as a real
partial (the faintest mark, 0.431 -> 0.404 against a 0.40 gate on the weakest of
its 3 corpus positives) and treats the gated pill's refusal to act as correct.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The faint-mask fallback added for the tophat front-end thresholded the
max-normalized uint8 response at 0.5 -- which selects every non-zero pixel,
not "half the peak" as its comment claimed -- and filled ~120% of the corner
box on textured frames. Measured on 14 real faint-path frames (cv2 fill,
detector re-run after): the detector's own best-match box fills a 58.7%-median
corner box vs 120.9% for the threshold, both 100% detector-clean. Detection and
the mask now read one method, _tophat_best, whose score gates detection and
whose argmax box bounds the fill, so the two cannot drift by construction --
which is how the mismatch arose. The 0.5 constant is deleted.
Parity could not catch this (a mask that fills everything is trivially
detector-clean) and the regression test could not either: its flat fixture
gives every threshold the same box, so mutating the constant to 99.0 stayed
green. The fixture now carries texture and asserts the mask area is bounded,
not merely non-empty; it reproduces the corpus number (127% pre-fix).
Also lands the Tier B2 verification harnesses that found and bounded this:
- detector_response.py: response curves (detected AND maskable per cell); found
the size response is a comb, contrast is near-irrelevant, no unmaskable cells.
- ladder_headroom.py: measured that a denser scale ladder recovers 7.6% of
misses for a 2.52%->3.05% false-fire rise, and the one landscape rung that
helps is a geometry shift that helps and hurts equally (1.7:1) -- do not add.
- cjk_tail_probe.py: a generic shared-tail (AI生成) template does not separate
uncovered vendors from clean corners (0.407 vs clean p99 0.298).
Records the visible-parity re-run confirming the earlier front-end fix (doubao
91.8% -> 99.3%), and dedups the thrice-written stamp forward model into one
fill_quality.composite.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The Space's demo code (app.py etc.) lives in a SEPARATE private repo,
wiltodelta/raiw-hf-space, which nothing documented -- so finding it cost a long
detour through the Space's commit authorship and a hunt for a write token that
never existed locally. Record it, plus the deploy flow that replaced the old
web-UI editing: push to that repo's main -> sync-to-hf.yml mirrors the files via
HfApi.upload_folder (adds a commit on top of the Space history, never a force
push). Also call out the two automations that both touch the Space so they are
not confused: sync-to-hf.yml ships demo-code changes, while this repo's
distribute.yml factory-rebuilds the Space on a library release so its
`remove-ai-watermarks>=` pin re-resolves.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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