# Harmful-Contents Dataset

A multi-label image dataset for harmful-content classification across eight PEGI-aligned categories.  
The dataset consists of 5,153 rights-cleared images, split into train/validation/test sets and annotated with both binary labels and mask fields for controlled negative sampling.

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## Dataset Structure

```
Harmful-Contents/
  csv/
    train.csv
    val.csv
    test.csv
  data/
    train/*.jpg
    val/*.jpg
    test/*.jpg
```

Each CSV contains:

```
name,
alcohol,drugs,weapons,gambling,nudity,sexy,smoking,violence,
mask_alcohol,mask_drugs,mask_weapons,mask_gambling,
mask_nudity,mask_sexy,mask_smoking,mask_violence
```

Images are stored in `data/{train,val,test}/` and referenced by name.

---

## Categories

| Category | Unsafe Examples | Safe Examples |
|---------|-----------------|---------------|
| **alcohol** | Alcohol bottles/glasses, alcohol brand logos | Empty glasses, non-alcoholic drinks |
| **drugs** | Cannabis, cocaine, pills, paraphernalia | OTC medication, neutral plants |
| **weapons** | Firearms, combat/attack knives, explosives | Kitchen knives, fruit knives, toy props |
| **gambling** | Casinos, slot machines, gambling chips/coins | Money, clovers, normal playing cards |
| **nudity** | Nudity, explicit sexual acts, pornography | Non-explicit partially clothed persons |
| **sexy** | Lingerie/underwear, sexualized posing | Sportswear, non-sexual clothing |
| **smoking** | Cigarettes, cigars, active smoking | Cigarette-like objects, steam/steam unrelated to smoking |
| **violence** | Blood, fighting, visible injury, aggression | Red liquids, non-violent crowds, hugging |

---

## Base Source (SIMAS)

The dataset is built using the **SIMAS** collection (*Spam Images for Malicious Annotation Set*) as the primary seed:  
https://zenodo.org/records/15423637

Additional rights-cleared images were added to improve class balance, yielding the final 5,153-image dataset described in the associated thesis.

---

## Loading With Hugging Face `datasets`

```python
from datasets import load_dataset, Image

data_files = {
    "train": "csv/train.csv",
    "validation": "csv/val.csv",
    "test": "csv/test.csv",
}

ds = load_dataset("csv", data_files=data_files)

def add_path(example, split):
    return {"image_path": f"data/{split}/{example['name']}"}

for split in ["train", "validation", "test"]:
    ds[split] = ds[split].map(lambda x, idx, s=split: add_path(x, s), with_indices=True)
    ds[split] = ds[split].cast_column("image_path", Image())
```

---

## License

Images are rights-cleared for **research and non-commercial use**.  
Commercial usage requires independent rights verification.

---

## Citation

If you use this dataset, please cite:

**Ulusoy, O.**  
*Evaluating and Fine-Tuning Vision Models for Keyword-Driven Content Filtering.*  
Bachelor Thesis, Flensburg University of Applied Sciences, 2025.