---
dataset_info:
  features:
    - name: image
      dtype: image
    - name: chart_type
      dtype: string
    - name: question
      dtype: string
    - name: question_class
      dtype: string
    - name: gold
      sequence: string
    - name: correct_or_misleading
      dtype: int64
    - name: misleader
      dtype: string
    - name: annotations
      sequence: string
  splits:
    - name: train
      num_examples: 6059
    - name: val
      num_examples: 6146
    - name: test
      num_examples: 6042
tags:
  - charts
  - data-visualization
  - visual-question-answering
  - image-classification
  - misleading-visualizations
  - arxiv:2601.12983
task_categories:
  - image-classification
  - visual-question-answering
pretty_name: AttackViz
---

# AttackViz

AttackViz is a chart-image dataset for studying correct and misleading data visualizations. It was introduced in the paper [ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation](https://arxiv.org/abs/2601.12983).

Each example contains a rendered chart image, metadata about the chart and question type, the expected gold answer, a binary label indicating whether the chart is correct or misleading, a misleading-visualization category, and serialized chart annotations.

## Dataset Structure

The dataset contains 18,247 examples across three splits:

| Split | Examples |
|---|---:|
| train | 6,059 |
| val | 6,146 |
| test | 6,042 |

## Fields

- `image`: chart image.
- `chart_type`: chart family. Values include `v_bar`, `h_bar`, and `line`.
- `question`: natural-language question associated with the chart.
- `question_class`: question category. Values include `compound`, `comparison`, `min_max`, `data_retrieval`, and `arithmetic`.
- `gold`: list of expected answer strings.
- `correct_or_misleading`: binary label where `0` means correct and `1` means misleading.
- `misleader`: misleading visualization type, or `none` for correct charts.
- `annotations`: list of serialized JSON strings containing the underlying chart specification and rendering metadata.

## Chart Types

| Type | Examples |
|---|---:|
| v_bar | 7,414 |
| h_bar | 7,348 |
| line | 3,485 |

## Question Classes

| Class | Examples |
|---|---:|
| compound | 4,581 |
| comparison | 4,258 |
| min_max | 4,172 |
| data_retrieval | 3,989 |
| arithmetic | 1,247 |

## Label Distribution

| Label | Meaning | Examples |
|---:|---|---:|
| 0 | correct | 6,373 |
| 1 | misleading | 11,874 |

## Misleading Types

| Type | Examples |
|---|---:|
| none | 6,373 |
| inappropriate_use_of_stacked | 2,689 |
| misrepresentation | 1,694 |
| inverted_axis | 1,674 |
| inappropriate_axis_range | 1,370 |
| 3d | 1,276 |
| inappropriate_use_of_log_scale | 942 |
| inappropriate_use_of_line | 548 |
| truncated_axis | 498 |
| ineffective_color_scheme | 498 |
| inappropriate_item_order | 460 |
| dual_axis | 225 |

## Loading

```python
from datasets import load_dataset

dataset = load_dataset("jgermanmx/AttackViz")
print(dataset)
print(dataset["train"][0])
```

## Intended Use

This dataset can be used to evaluate or train models for chart understanding, misleading visualization detection, visual question answering over charts, and analysis of how design choices affect interpretation.

## Citation

If you use AttackViz, please cite:

```bibtex
@misc{ortizbarajas2026chartattack,
  title = {ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation},
  author = {Jesus-German Ortiz-Barajas and Jonathan Tonglet and Vivek Gupta and Iryna Gurevych},
  year = {2026},
  eprint = {2601.12983},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  doi = {10.48550/arXiv.2601.12983},
  url = {https://arxiv.org/abs/2601.12983}
}
```

## Notes

The `annotations` field stores JSON as strings. Parse individual entries with `json.loads` when structured access to chart specifications is needed.