Datasets:

MTSAIR
/

MWS-Antifraud-Bench

license: other
license_name: mws-antifraud-research-only-1.0
license_link: LICENSE.md
task_categories:
  - image-classification
  - visual-question-answering
language:
  - ru
tags:
  - antifraud
  - document-verification
  - benchmark
  - multimodal
pretty_name: MWS Antifraud Bench (Validation)
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: dataset_name
        dtype: string
      - name: question
        dtype: string
      - name: answers
        list: string
      - name: image
        dtype: image
    splits:
      - name: train
        num_bytes: 102549007
        num_examples: 209
    download_size: 102260438
    dataset_size: 102549007
  - config_name: en
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: dataset_name
        dtype: string
      - name: question
        dtype: string
      - name: answers
        list: string
      - name: image
        dtype: image
    splits:
      - name: train
        num_bytes: 102449014
        num_examples: 209
    download_size: 102255220
    dataset_size: 102449014
  - config_name: zh
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: dataset_name
        dtype: string
      - name: question
        dtype: string
      - name: answers
        list: string
      - name: image
        dtype: image
    splits:
      - name: train
        num_bytes: 102445041
        num_examples: 209
    download_size: 102255620
    dataset_size: 102445041
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
  - config_name: zh
    data_files:
      - split: train
        path: zh/train-*

MWS Antifraud Bench (Validation)

Experimental document-authenticity task for general-purpose multimodal language models. This is the public validation part of MWS Vision Bench anti-fraud v0.1.

The dataset is released for research and model comparison. It is not a certification tool, a production fraud-detection system, or a universal leaderboard that is expected to be resistant to deliberate optimization.

Data

The validation split contains 209 items:

44 ai_gen;
65 edited;
100 original.

original means an unmodified source image in this experiment. It must not be interpreted as a legal authenticity judgment.

Each item contains:

id;
type;
dataset_name — one of original, edited, ai_gen;
question;
answers — annotated edited fields when available;
image.

Language configs

The same images are available with three question-language configs:

default — Russian;
en — English;
zh — Chinese.

from datasets import load_dataset

antifraud_ru = load_dataset("MTSAIR/MWS-Antifraud-Bench")
antifraud_en = load_dataset("MTSAIR/MWS-Antifraud-Bench", "en")
antifraud_zh = load_dataset("MTSAIR/MWS-Antifraud-Bench", "zh")

Expected answer

The model must return one JSON object:

{"label": "original|edited|ai_gen", "arguments": "short explanation"}

Metric

The metric combines balanced three-class accuracy and the quality of the explanation for manually edited documents:

AF = 0.75 × max(0, balanced_accuracy − 1/3)
     + 0.5 × edited_reason_score

AF is aggregated over the complete split. Balanced accuracy requires the full three-class confusion matrix; unlike the five original MWS Vision Bench metrics, it is not a plain mean of independent per-item scores.

Anti-fraud is reported as a separate leaderboard category and is excluded from the primary MWS Vision Bench Overall score.

Leaderboard (Validation)

The table mirrors the Anti-fraud column in the canonical MWS Vision Bench leaderboard and is sorted by the anti-fraud score. This score is reported separately and is not included in Overall.

Model	Anti-fraud
Claude Fable 5	0.521
GPT-5.5	0.477
GPT-5.6 Sol	0.467
GPT-5.4	0.432
GPT-5.2	0.413
GPT-5.6 Terra	0.404
Kimi K3	0.400
Claude Sonnet 5	0.391
Claude-4.6-Opus	0.385
Kimi K2 Instruct	0.331
Cotype Light 3	0.318
Claude-4.5-Opus	0.308
GPT-5.6 Luna	0.302
Qwen3-VL-32B-Instruct	0.298
Claude-4.5-Sonnet	0.280
Kimi K2.6	0.279
Qwen3.5-2B	0.277
Qwen 3.7 Plus	0.250
Claude Sonnet 4.6	0.235
Cotype Pro 3	0.233
GPT-5-mini	0.233
Qwen3.6-35B-A3B	0.232
Gemini-3.1-flash-lite-preview	0.217
Gemini 3.5 Flash	0.216
Mistral Large 3 2512	0.215
Qwen3-VL-8B-Instruct	0.196
Qwen3.6-27B	0.195
Qwen3.5-27B	0.190
Qwen3.5-0.8B	0.185
GLM-5V Turbo	0.183
Qwen3.5-9B	0.178
GPT-4.1-mini	0.173
Qwen3-VL-235B-A22B-Instruct	0.171
Gemini-3-flash-preview	0.153
Qwen3.5-35B-A3B	0.142
Gemini-2.5-flash	0.137
GPT-4.1	0.131
Mistral Small 3.2 24B Instruct	0.122
Gemma 4 31B IT	0.119
Gemini 3.6 Flash	0.115
Qwen3.5-4B	0.100
GPT-5.1	0.096
Qwen2.5-VL-72B-Instruct	0.091
Gemini-2.5-pro	0.088
Cotype VL (32B 8 bit)	0.078
Alice AI VLM dev	0.046

Limitations

This is an experimental research dataset. Its class balance and document-type distribution differ from a production stream and can provide shortcuts to a system tuned specifically for this collection. A motivated participant can optimize against the benchmark. Use the score to explore and compare models for your own scenario, not as a universal measure of document authenticity.

License and allowed use

This dataset is distributed under the MWS Antifraud Research-Only License 1.0. The dataset is intended for research and model comparison and is not a certification or production fraud-detection system.

The paragraph above is a summary. If it conflicts with LICENSE.md, the license text controls.