---
license: mit
task_categories:
- image-classification
- image-segmentation
tags:
- deepfake-detection
- image-manipulation
- forensics
- genai-detection
- real-estate
- interior-design
- hackathon
size_categories:
- 1K<n<10K
pretty_name: GenAI Manipulation Detection Dataset - Interior Design Images
---
# GenAI Manipulation Detection Dataset - Interior Design Images

## 📋 Dataset Description

This dataset contains **1000 paired images** (real + manipulated) for training and evaluating GenAI manipulation detection models. Created for the **MenaML Winter School 2026 Hackathon**.

### Dataset Summary

- **Total Images**: 1000 pairs (2000 total images)
- **Image Size**: 512x512
- **Format**: JPEG
- **Source**: Pinterest Interior Design Images (Kaggle)
- **License**: MIT

## 🎯 Challenge Context

This dataset was created for **Track B: Real Estate & Commercial Integrity** of the MenaML Winter School 2026 GenAI Detection Challenge.

The challenge focuses on detecting:
- ✅ Virtual staging (furniture replacement)
- ✅ Texture smoothing (wall/surface manipulation)
- ✅ Compression artifacts
- ✅ Splicing and copy-move forgery
- ✅ Physical impossibilities (shadow/reflection mismatches)

## 📂 Dataset Structure

```
dataset/
├── data/
│   ├── real/              # Original unmanipulated images
│   │   ├── real_000000.jpg
│   │   ├── real_000001.jpg
│   │   └── ...
│   └── fake/              # Manipulated images
│       ├── fake_000000.jpg
│       ├── fake_000001.jpg
│       └── ...
├── annotations.csv               # ⭐ Main annotations (2 rows per pair)
├── detailed_annotations.json     # Paired format annotations
├── metadata.json                 # Dataset statistics
└── README.md                     # This file
```

## 📊 Annotations Format

### CSV Annotations (`annotations.csv`)

Each image pair creates **2 rows** - one for real, one for fake:

```csv
file_name,image_path,label,is_manipulated,manipulation_category,manipulation_technique,manipulation_description,image_id,pair_id
real_000000.jpg,data/real/real_000000.jpg,real,0,none,none,Authentic unmanipulated image,000000,000000
fake_000000.jpg,data/fake/fake_000000.jpg,fake,1,smoothness_anomaly,bilateral_filter,Unnatural smoothness in walls/surfaces,000000,000000
real_000001.jpg,data/real/real_000001.jpg,real,0,none,none,Authentic unmanipulated image,000001,000001
fake_000001.jpg,data/fake/fake_000001.jpg,fake,1,compression_artifact,double_jpeg,Double JPEG compression,000001,000001
```

**Columns:**
- `file_name`: Image filename
- `image_path`: Relative path to image
- `label`: "real" or "fake"
- `is_manipulated`: 0 (real) or 1 (fake)
- `manipulation_category`: Category (or "none" for real images)
  - smoothness_anomaly
  - compression_artifact
  - frequency_manipulation
  - splicing
  - physical_impossibility
- `manipulation_technique`: Specific technique (or "none" for real images)
- `manipulation_description`: Human-readable description
- `image_id`: Unique ID for this image
- `pair_id`: ID linking real and fake pairs (same for both images in a pair)

### JSON Annotations (`detailed_annotations.json`)
Paired format for easier processing:
```json
[
  {
    "pair_id": "000000",
    "real_image": {
      "filename": "real_000000.jpg",
      "path": "data/real/real_000000.jpg",
      "label": "real"
    },
    "fake_image": {
      "filename": "fake_000000.jpg",
      "path": "data/fake/fake_000000.jpg",
      "label": "fake",
      "manipulation": {
        "category": "smoothness_anomaly",
        "technique": "bilateral_filter",
        "description": "Unnatural smoothness in walls/surfaces"
      }
    }
  }
]
```
## 📊 Manipulation Categories

- **compression_artifact**: 191 images (19.1%)
- **smoothness_anomaly**: 206 images (20.6%)
- **physical_impossibility**: 192 images (19.2%)
- **frequency_manipulation**: 209 images (20.9%)
- **splicing**: 202 images (20.2%)


### Detailed Technique Breakdown

- `compression_mismatch`: 100 (10.0%)
- `bilateral_filter`: 108 (10.8%)
- `texture_removal`: 98 (9.8%)
- `reflection_inconsistency`: 94 (9.4%)
- `upscaling`: 74 (7.4%)
- `copy_move`: 94 (9.4%)
- `frequency_injection`: 70 (7.0%)
- `object_insertion`: 108 (10.8%)
- `grid_artifact`: 65 (6.5%)
- `shadow_mismatch`: 98 (9.8%)
- `double_jpeg`: 91 (9.1%)


## 🔧 Manipulation Techniques Explained

### 1️⃣ Smoothness Anomaly
- **bilateral_filter**: Aggressive bilateral filtering creating unnatural smoothness
- **texture_removal**: Edge-preserving filter that removes texture detail
- **Detection**: Texture analysis, high-frequency loss detection

### 2️⃣ Compression Artifact
- **double_jpeg**: Two rounds of JPEG compression with different quality levels
- **compression_mismatch**: Regions with different compression quality
- **Detection**: DCT coefficient analysis, block artifact detection

### 3️⃣ Frequency Manipulation
- **upscaling**: Downscale→upscale creating bicubic interpolation signatures
- **frequency_injection**: GAN-like ring patterns in frequency domain
- **grid_artifact**: 8×8 grid patterns typical of GAN outputs
- **Detection**: FFT analysis, power spectral density

### 4️⃣ Splicing
- **copy_move**: Copy region and paste elsewhere with compression mismatch
- **object_insertion**: Insert objects with different compression characteristics
- **Detection**: SIFT/ORB feature matching, noise inconsistency analysis

### 5️⃣ Physical Impossibility
- **shadow_mismatch**: Inconsistent shadow directions
- **reflection_inconsistency**: Reflections not matching room layout
- **Detection**: VLM reasoning, physics-based validation

## 💻 Usage

### Load All Data

```python
import pandas as pd
from PIL import Image
# Load annotations
df = pd.read_csv("hf://datasets/FatimahEmadEldin/genai-manipulation-detection-interior/annotations.csv")
print(f"Total images: {len(df)}")
print(f"Real images: {len(df[df['label'] == 'real'])}")
print(f"Fake images: {len(df[df['label'] == 'fake'])}")
# Show manipulation distribution (fake images only)
fakes = df[df['label'] == 'fake']
print("\nManipulation categories:")
print(fakes['manipulation_category'].value_counts())
```

### Filter by Manipulation Type

```python
# Get only images with specific manipulation
smoothness_fakes = df[df['manipulation_category'] == 'smoothness_anomaly']
print(f"Smoothness anomalies: {len(smoothness_fakes)}")
# Get specific technique
bilateral_images = df[df['manipulation_technique'] == 'bilateral_filter']
print(f"Bilateral filter: {len(bilateral_images)}")
# Get all compression artifacts
compression = df[df['manipulation_category'] == 'compression_artifact']
techniques = compression['manipulation_technique'].value_counts()
print(techniques)
```

### Load Image Pairs

```python
# Get a pair by pair_id
pair_id = "000000"
pair = df[df['pair_id'] == pair_id]
real_row = pair[pair['label'] == 'real'].iloc[0]
fake_row = pair[pair['label'] == 'fake'].iloc[0]
real_img = Image.open(f"hf://datasets/FatimahEmadEldin/genai-manipulation-detection-interior/{real_row['image_path']}")
fake_img = Image.open(f"hf://datasets/FatimahEmadEldin/genai-manipulation-detection-interior/{fake_row['image_path']}")
print(f"Real: {real_row['file_name']}")
print(f"Fake: {fake_row['file_name']}")
print(f"Manipulation: {fake_row['manipulation_technique']}")
print(f"Description: {fake_row['manipulation_description']}")
```

### Training with Class Labels

```python
# Create dataset with manipulation classes
fakes_only = df[df['label'] == 'fake'].copy()
# Map techniques to numeric labels
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
fakes_only['class_label'] = le.fit_transform(fakes_only['manipulation_technique'])
print("Class mapping:")
for i, tech in enumerate(le.classes_):
    print(f"  {i}: {tech}")
# Use for training
from torch.utils.data import Dataset
class ManipulationDataset(Dataset):
    def __init__(self, dataframe, transform=None):
        self.df = dataframe.reset_index(drop=True)
        self.transform = transform
        
    def __len__(self):
        return len(self.df)
    
    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        img = Image.open(row['image_path'])
        
        if self.transform:
            img = self.transform(img)
        
        return {
            'image': img,
            'label': row['class_label'],
            'category': row['manipulation_category'],
            'technique': row['manipulation_technique']
        }
```

### Binary Classification (Real vs Fake)

```python
# Simple real vs fake
df['binary_label'] = df['is_manipulated']  # 0 = real, 1 = fake
# Or use the label column
df['binary_label'] = (df['label'] == 'fake').astype(int)
# Split by label
real_images = df[df['binary_label'] == 0]
fake_images = df[df['binary_label'] == 1]
```

### Multi-task Learning

```python
# Train on both binary and multiclass
class MultiTaskDataset(Dataset):
    def __init__(self, dataframe, technique_to_idx, transform=None):
        self.df = dataframe.reset_index(drop=True)
        self.technique_to_idx = technique_to_idx
        self.transform = transform
        
    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        img = Image.open(row['image_path'])
        
        if self.transform:
            img = self.transform(img)
        
        # Binary label
        binary_label = row['is_manipulated']
        
        # Multiclass label (only for fake images)
        if binary_label == 1:
            multiclass_label = self.technique_to_idx[row['manipulation_technique']]
        else:
            multiclass_label = -1  # Ignore for real images
        
        return {
            'image': img,
            'binary_label': binary_label,
            'multiclass_label': multiclass_label,
            'category': row['manipulation_category']
        }
```

## 📝 Annotations Format

The dataset includes **two annotation files** for different use cases:

### 1. `annotations.csv` - Flat Format

Contains **2 entries per pair** (one for real, one for fake):

| Column | Description | Example Values |
|--------|-------------|----------------|
| `file_name` | Image filename | `real_000000.jpg`, `fake_000000.jpg` |
| `image_path` | Relative path | `data/real/real_000000.jpg` |
| `label` | Real or fake | `real`, `fake` |
| `is_manipulated` | Binary flag | `0` (real), `1` (fake) |
| `manipulation_category` | Category | `smoothness_anomaly`, `compression_artifact`, etc. |
| `manipulation_technique` | Specific technique | `bilateral_filter`, `double_jpeg`, etc. |
| `manipulation_description` | Human description | "Unnatural smoothness in walls..." |
| `image_id` | Unique image ID | `000000` |
| `pair_id` | Links real/fake pairs | `000000` (same for both in pair) |

**Use this for:**
- Quick filtering by label or technique
- Training binary classifiers
- Training multiclass classifiers
- Statistical analysis

### 2. `detailed_annotations.json` - Paired Format
Contains paired structure (1 entry per pair):
**Use this for:**
- Contrastive learning
- Side-by-side comparison
- Paired image processing
### Manipulation Categories
1. **smoothness_anomaly** (20%)
   - `bilateral_filter`: Aggressive bilateral filtering
   - `texture_removal`: Edge-preserving texture removal

2. **compression_artifact** (20%)
   - `double_jpeg`: Multiple JPEG compression
   - `compression_mismatch`: Regional quality differences
3. **frequency_manipulation** (20%)
   - `upscaling`: Bicubic upscaling artifacts
   - `frequency_injection`: GAN-like frequency patterns
   - `grid_artifact`: 8×8 grid patterns

4. **splicing** (20%)
   - `copy_move`: Copy-paste forgery
   - `object_insertion`: Object insertion with mismatches

5. **physical_impossibility** (20%)
   - `shadow_mismatch`: Inconsistent shadow directions
   - `reflection_inconsistency`: Impossible reflections
     
## 🎓 Citation

If you use this dataset in your research or hackathon submission, please cite:

```
@dataset{genai_manipulation_interior_2026,
  title={GenAI Manipulation Detection Dataset - Interior Design},
  author={MenaML Winter School 2026 - Team ArtifactDetect},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/FatimahEmadEldin/genai-manipulation-detection-interior}}
}

```


## 📜 License

This dataset is released under the **MIT License**.
The source images are from the Pinterest Interior Design Images dataset on Kaggle, used under MIT license.
## 🏆 Hackathon Information
- **Event**: MenaML Winter School 2026
- **Challenge**: Detecting GenAI & Sophisticated Manipulation in Public Media
- **Track**: B - Real Estate & Commercial Integrity
- **Deadline**: January 28, 2026


## 🙏 Acknowledgments
- Source dataset: [Pinterest Interior Design Images](https://www.kaggle.com/datasets/galinakg/interior-design-images-and-metadata) by Galina KG
- Challenge organizers: MenaML Winter School 2026
- Tools: OpenCV, scikit-image, NumPy, Pandas