Joshyxwa/data_draft

TechJam 2026 Data Draft

Dataset Summary

This private research draft validates a binary image-classifier pipeline: the local data loader, feature extractor, classifier head, calibration step, and robustness evaluation. It is not a public benchmark, a claim of real-world detection quality, or part of the production 80k-master corpus.

The package has 10,000 canonical RGB PNG images. Every image has a portable manifest record with a label, split, source provenance, hashes, image properties, and processing history.

Composition

WildFake real examples are evenly selected from afhq, celebahq, church, ffhq, and imagenet. Fully-AI examples are evenly selected from adm, ddim, ddpm, imagen, and gan_based. Each WildFake family contributes 500 images. COCO and DALL-E families are excluded.

Splits

Do not move records between splits or train on dev/calibration images or their derivatives. The builder rejects cross-split collisions by asset ID, base ID, lineage ID, exact SHA-256, exact perceptual hash, and near perceptual hash distance at most five during WildFake selection.

Labels

Labels are source-provided, not manually re-annotated. label_evidence and label_confidence are present for every row; the confidence is 0.8 for this draft. There are no partial_ai, recaptured_ai, or edited-image labels.

Files And Metadata

manifest.parquet includes these field groups:

Paths in the manifest are relative to this dataset repository. After download:

from pathlib import Path

import pandas as pd
from PIL import Image

root = Path("path/to/data_draft")
manifest = pd.read_parquet(root / "manifest.parquet")
row = manifest.iloc[0]
image = Image.open(root / row.path).convert("RGB")
label = row.label  # "real" or "ai_full"

Quality Checks

The packaged manifest passed the following local checks:

• —10,000 of 10,000 images decode as canonical RGB files.
• —Recorded SHA-256, perceptual hash, width, and height match every image file.
• —No detected cross-split collision by base ID, lineage ID, parent ID, exact hash, or exact perceptual hash.
• —Final class counts are exactly balanced in every split.
• —The package contains no raw source archives, API keys, model checkpoints, production generations, COCO records, or DALL-E records.

Provenance And Restrictions

SID-Set rows retain upstream source URI and CC-BY-4.0 metadata. WildFake rows retain the exact ZIP archive and member path but are marked licence_audit_required. Therefore:

• —Keep this repository private.
• —Do not redistribute WildFake-derived files or make public demos from them until the upstream licence review is complete.
• —Do not add these records to the production 80k corpus or use them for benchmark/generalization claims.

The other licence tag describes the mixed, restricted state of the package; it grants no rights beyond the underlying source datasets.

Limitations

This small mixture is useful for pipeline and architecture smoke tests, but it is source-family constrained. Its results may be optimistic because training, development, and calibration all originate from the same two upstream datasets. It does not model social-media reposting, video-frame extraction, partial edits, recapture, contemporary held-out generators, or real-world class prevalence. Report per-source and per-transform errors, not only aggregate AUROC/AUPRC.

Reproducibility

The deterministic selection seed is 20260830. manifest.parquet preserves the selection and processing lineage, stable asset/base/lineage identifiers, and content hashes required to audit a local rebuild before training.