Kled: Financial & Legal Documents
Collection
Native PDFs and photographed paper from real finances and contracts: statements, invoices, payslips, leases, receipts, tax forms. • 14 items • Updated
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Dataset Card for Forged Document Pairs
Pairs of a suspected forgery and a genuine document of the same class, altered regions boxed. No ID documents.
Collected through the Kled app by consenting contributors and licensed for commercial training. The files are delivered by Kled under license, not downloaded from this repository. Use Request a sample set at the top of this page, or write to team@kled.ai. Catalog entry: kled.ai/datasets/document-forgery-pairs.
Dataset Details
Dataset Description
Choose the document class, upload a document you believe is forged with a genuine one of the same type, box what looks altered and describe it. Passports, licenses and any identity document are refused; self-made forgeries and unpaired submissions are sent back. One pair per contributor, as PDF, from a global base strongest in Southeast Asia, the United States and Europe. Each pair gives the same document class in genuine and suspect form with a human-localized region and stated rationale, supervision forensics models cannot get from synthetic edits; DocTamper is programmatic edits, academic-only. Suspicion is the contributor's, not an examiner's; the localization is the stronger signal.
- Curated by: Kled (useKled)
- Collected through: File upload from phone
- Language(s): English, Indonesian, Malay, Filipino, Spanish, French, Hindi
- Regions: Global, 100+ countries
- Volume: Available on request
- Contributors: Available on request
- Modality: Text
- Format: PDF
- Resolution: Native PDF, text layer where the source system produced one
- Annotations: Labeled (boxed alterations, description)
- Quality control: QA review on every submission
- License: Non-exclusive commercial training license (terms); exclusivity available on request
- Consent: Each contributor opted in to this use when they uploaded.
Dataset Sources
- Catalog page: kled.ai/datasets/document-forgery-pairs
- Sample set: through the access request at the top of this page, or team@kled.ai
- Publisher: huggingface.co/useKled
Uses
Direct Use
- Document forgery detection and tamper localization
- Forensic model evaluation
- Document-class authenticity
Out-of-Scope Use
Redistribution or resale of the files, use outside the terms of the commercial training license, and any attempt to identify or contact contributors or people who appear in the data. Uses that require labels this set does not carry should be scoped with the data team first.
Dataset Structure
Data Instances
One document per record, delivered as PDF. Volume: Available on request.
Data Fields
- File: the PDF file as captured or uploaded by the contributor
- Labels: Labeled (boxed alterations, description), entered by the contributor at capture and checked at review.
- Capture metadata: capture tool, country of capture, submission time
- Review status: QA outcome (QA review on every submission)
Field names and the manifest format are confirmed with the sample set.
Data Splits
Delivered as one set. Subsets by country, language or label, and custom splits, are available on request.
Dataset Creation
Curation Rationale
The dataset exists because the public alternatives named in the description are non-commercial, synthetic, small or collected elsewhere. Kled runs the task as a paid brief in its app so that the data is captured on purpose, under the capture rules above, by people who have opted in to commercial use.
Source Data
Data Collection and Processing
Contributors complete the task in the Kled app. Capture tool: File upload from phone. Each submission is reviewed against the task brief; submissions that break the capture rules are rejected. QA review on every submission. Files are delivered as PDF, in the resolution noted above.
Who are the source data producers?
Kled contributors, who complete paid data tasks on their own phones. This task was open to contributors worldwide. Kled has 625,000+ contributors in 100+ countries, strongest in Southeast Asia, the United States and Europe.
Annotations
Annotation process
Labeled (boxed alterations, description). The label is entered by the contributor as part of the task and checked at review against the task brief; it is a contributor label, not an expert annotation, unless the description above says otherwise.
Who are the annotators?
The contributors themselves, at the moment of capture. Kled reviewers check each submission against the task brief.
Personal and Sensitive Information
Every item was contributed by its owner, who opted in to this use at upload. The task brief states what may and may not appear in a submission, and reviewers reject submissions that break those rules. Kled's data handling practices are documented at trust.kled.ai; questions about handling requirements for your use go to team@kled.ai.
Bias, Risks, and Limitations
Coverage follows the contributor base: strongest in Southeast Asia, the United States and Europe, thinner elsewhere, and not limited by region for this task. Devices, settings and conditions vary by contributor, which is the point of the collection and also means no two items are captured under identical conditions. Where labels are contributor-entered, they are checked at review but are not expert annotations. Volumes are the current scope of the task and grow as collection continues.
Recommendations
Request a sample set and run it against your task before licensing. Ask for per-country or per-language counts where they matter to your use.
Licensing Information
Non-exclusive commercial training license; exclusivity on request. Each contributor opted in to this use when they uploaded. The license terms are on the catalog page.
Citation
@misc{kled_document_forgery_pairs_2026,
title = {Forged Document Pairs},
author = {Kled},
year = {2026},
url = {https://www.kled.ai/datasets/document-forgery-pairs},
note = {Commercially licensed dataset collected through the Kled app. Sample sets on request.}
}
Dataset Card Contact
Kled data team: team@kled.ai
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