Datasets:

manj0220
/

pdf-steganalysis-corpus

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cover_00000836a41510292765a86e57a80f1e182360ca5c6ff0841482b31c8f25ab84	cover_00000836a41510292765a86e57a80f1e182360ca5c6ff0841482b31c8f25ab84	00000836a41510292765a86e57a80f1e182360ca5c6ff0841482b31c8f25ab84	historical	clean	train	1	0.684641	7.111222	5.827451	255	1	29,690	9	0	0	0	335	9	1,334	42	0	0	12	419.528	297.638	42	13	1	42	0	1	26	2	1	5.150939	13	91,321	17,729	1	0.092759	1	0
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cover_00000bae03b708d17d071c1fe0d21b7d77cf5c1c763dc36f82d68a1eca23b2b4	cover_00000bae03b708d17d071c1fe0d21b7d77cf5c1c763dc36f82d68a1eca23b2b4	00000bae03b708d17d071c1fe0d21b7d77cf5c1c763dc36f82d68a1eca23b2b4	historical	clean	train	6	0.788017	6.271573	3.666667	33	1	3,939	3	0	0	0	101	4	223	25	0	0	1	841.89	595.28	23	1	1	23	0	1	2	2	1	3.056034	1	2,836	928	1	0.145215	0	0
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cover_00002bfc74a08fa82de8c2f3f0953023a482526f7b5b02f64359ca091e0de039	cover_00002bfc74a08fa82de8c2f3f0953023a482526f7b5b02f64359ca091e0de039	00002bfc74a08fa82de8c2f3f0953023a482526f7b5b02f64359ca091e0de039	historical	clean	train	9	0.411706	7.949099	5.396552	58	1	249,659	4	0	6	0	192	8	6,061	72	0	0	2	840.579354	593.823132	72	14	1	72	0	1	28	2	1	14.78301	14	3,459,594	234,025	2	0.036193	1	0
cover_00002f9c882e7d2da9a6966706ec2386490ec5fc5db7139d5449746fda2ceb47	cover_00002f9c882e7d2da9a6966706ec2386490ec5fc5db7139d5449746fda2ceb47	00002f9c882e7d2da9a6966706ec2386490ec5fc5db7139d5449746fda2ceb47	historical	clean	train	0	0.455528	7.862982	1.021033	2,710	2	8,621,111	12	0	4	1	97	5	193,105	82	1	0	2	841.89	1,190.55	52	48	2	52	5	2	96	2	2	null	48	null	8,615,081	2	0.055581	1	1
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End of preview. Expand in Data Studio

PDF Steganalysis Corpus v2

A research benchmark for clean-versus-stego detection and method attribution, plus a separate downloadable demonstration made from original synthetic PDFs. "Clean" means the original document before our embedding, not malware-free. This is not a malware collection.

What to download

Files	Purpose
`historical-{train,validation,test}.parquet`	13,637 verified historical stego records + 1,939 clean originals, with labels and 38 numeric features. No historical PDFs.
`synthetic-{train,validation,test}.parquet`	Labels, features, hashes and ZIP paths for 120 original covers + 2,284 passing stego PDFs.
synthetic-clean.zip	The 120 original synthetic covers.
synthetic-stego.zip	The 2,284 accepted synthetic stego PDFs.
benchmark-details.zip	Task/pair indexes, all audits, method counts, baseline results/scripts, schema and detailed documentation.
LICENSE.md, CITATION.cff, SHA256SUMS-v2	Licensing, citation and file-integrity checks.

Load the datasets

from datasets import load_dataset

historical = load_dataset("manj0220/pdf-steganalysis-corpus", "historical_v2")
synthetic = load_dataset("manj0220/pdf-steganalysis-corpus", "synthetic_v2")
# Pin revision="<published dataset commit>" in experiments.

Unzip benchmark-details.zip into the same directory as the six Parquet files and two PDF archives. It adds the supporting files without duplicating those large downloads. Then:

python3 -m venv .venv
.venv/bin/python -m pip install -r requirements-lock.txt
.venv/bin/python verify_release.py --data .
.venv/bin/python baselines.py --data . --out recomputed-results

Use task-index.parquet for balanced matched binary tasks, and all verified stego records for attribution. Predictors are only the 38 single-document numeric columns allowlisted in summary-v2.json. Do not train on IDs, labels, hashes, statuses, split, layout, payload size or paired deltas. Paired deltas are supplied separately for analyses that have access to the original cover.

Counts and limits

All 32,456 historical sample records remain in historical-audit.parquet inside the details ZIP, including exclusions. Primary eligibility requires corroborated cover provenance, correct hashes, unchanged source bytes, strict parsing, matching page counts and exact decoded payload digest equality.

Historical covers come from Digital Corpora CC-MAIN-2021-31-PDF-UNTRUNCATED, archives 0000/0001. Numeric archive basenames, SHA-256 and aligned legacy original-cover MD5 corroborate provenance; MD5 is not a collision-resistant chain of custody. The original crawl-derived PDFs and their stego derivatives are not redistributed. Their rights are not covered by this release license.

No explicit historical key was supplied. Consequently, 15,036 records across hybrid, orphanstream, ws, numfmt and opsyn are excluded from the verified track, not declared invalid. Historical XMP has 3,460 strict-parser failures and 64 eligible samples (60 train, zero validation, four test). Its binary baseline is skipped for absent validation support. Failures never become clean examples.

Historical derivatives are grouped by cover hash; identical-byte groups are merged before approximately 80/10/10 splitting. 131 near-duplicate cover pairs are flagged, not adjudicated. v1-v2-mapping.parquet documents changed splits. Source hashes reconcile for all 32,456 outputs and 2,000 recovered covers.

The synthetic demo attempted 11 methods x 120 covers x 32/128/512-byte benign ASCII messages. Eight methods produced the 2,284 accepted outputs; A0, TJ and operator-LSB produced none on this grid. All accepted stego PDFs pass parsing, all-page low-resolution rendering, exact payload round-trip and integrity checks. The 120 covers use original text, tables, vectors, procedural images, numeric content and mixed two-page layouts. No external image or document assets were used.

Synthetic results are not evidence of real-document generalization. Rendering success does not certify visual equivalence or malware safety. Regeneration is functionally tested; byte equality is limited to tested cases, not universally promised. The new public demonstration key is in synthetic-summary.json; no historical payload text, historical key or private configuration is included.

RESULTS-v2.md and baseline-results.json report logistic regression and random forest, full-feature and size-only controls, seed 20260921, train-only preprocessing/class weighting, validation-selected thresholds and held-out test metrics/supports/confusion matrices. Small support, shared message categories, serialization artifacts and potential near duplicates limit generalization.

Tools and release status

This compact package includes newly authored baseline and table/archive checking scripts under MIT. Existing embedding/extraction modules are not included: their redistribution awaits maintainer license confirmation. Generation commands in REPRODUCIBILITY-v2.md describe the staged standalone tool, not an available download in this package. This data package does not claim that the complete generation-tool release has been published. Dependency licenses remain separate.

Previous version

v1 files are not duplicated here. They remain available at the pinned Hugging Face v1 revision and GitHub v1 revision. For old samples or attempts loading calls, explicitly pin the v1 revision. Only historical_v2 and synthetic_v2 are viewer configurations in this compact package; audit tables are in benchmark-details.zip.

License, citation and contact

Data, documentation and original synthetic content: CC BY 4.0; see LICENSE.md. New scripts: MIT. Dependencies retain their own licenses. Defensive research is the intended use, not an additional research-only license restriction. Steganography is dual-use; this is not an exploit-payload pack. Process unfamiliar PDFs in an isolated environment.

Maintainer: Manan Jain. Report labeling/provenance/privacy concerns through GitHub issues. Use CITATION.cff and the exact dataset revision. No unverified paper DOI or publication status is asserted.

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