Initial upload: GenAI manipulation detection dataset

README.md

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+---
+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        # Detailed annotations for each image pair
+├── metadata.json         # Dataset statistics and metadata
+└── README.md            # This file
+```
+
+## 📊 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 with Pandas
+
+```python
+import pandas as pd
+from PIL import Image
+
+# Load annotations
+df = pd.read_csv("hf://datasets/FatimahEmadEldin/genai-manipulation-detection-interior/annotations.csv")
+
+# Load an image pair
+row = df.iloc[0]
+
+real_img = Image.open(f"hf://datasets/FatimahEmadEldin/genai-manipulation-detection-interior/data/{row['real_image_path']}")
+fake_img = Image.open(f"hf://datasets/FatimahEmadEldin/genai-manipulation-detection-interior/data/{row['fake_image_path']}")
+
+print(f"Manipulation: {row['manipulation_category']}")
+print(f"Technique: {row['manipulation_technique']}")
+```
+
+### Load with HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("FatimahEmadEldin/genai-manipulation-detection-interior")
+
+# Access data
+sample = dataset['train'][0]
+```
+
+### PyTorch DataLoader
+
+```python
+from torch.utils.data import Dataset, DataLoader
+import pandas as pd
+from PIL import Image
+
+class ManipulationDataset(Dataset):
+    def __init__(self, annotations_csv, data_dir, transform=None):
+        self.df = pd.read_csv(annotations_csv)
+        self.data_dir = data_dir
+        self.transform = transform
+        
+    def __len__(self):
+        return len(self.df)
+    
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        
+        real_img = Image.open(f"{self.data_dir}/{row['real_image_path']}")
+        fake_img = Image.open(f"{self.data_dir}/{row['fake_image_path']}")
+        
+        if self.transform:
+            real_img = self.transform(real_img)
+            fake_img = self.transform(fake_img)
+        
+        return {
+            'real': real_img,
+            'fake': fake_img,
+            'label': 1,  # 1 for manipulated
+            'category': row['manipulation_category'],
+            'technique': row['manipulation_technique']
+        }
+
+# Usage
+dataset = ManipulationDataset('annotations.csv', 'data/')
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+```
+
+## 📝 Annotations Format
+
+Each row in `annotations.csv` contains:
+
+- `image_id`: Unique identifier (e.g., "000000")
+- `real_image_path`: Path to real image (e.g., "real/real_000000.jpg")
+- `fake_image_path`: Path to manipulated image (e.g., "fake/fake_000000.jpg")
+- `real_image_filename`: Filename of real image
+- `fake_image_filename`: Filename of manipulated image
+- `source_image_path`: Original source from Kaggle dataset
+- `manipulation_category`: High-level category (5 types)
+- `manipulation_technique`: Specific technique used
+- `manipulation_description`: Human-readable description
+- `label`: Always "manipulated"
+- `timestamp`: Creation timestamp
+
+## 🎓 Citation
+
+If you use this dataset in your research or hackathon submission, please cite:
+
+```bibtex
+@dataset{genai_manipulation_interior_2026,
+  title={GenAI Manipulation Detection Dataset - Interior Design},
+  author={MenaML Winter School 2026 - Team [Your Team Name]},
+  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
+
+## 📧 Contact
+
+For questions about this dataset, please open an issue on the HuggingFace dataset page.
+
+---
+
+**Created**: 2026-01-26T22:36:29.871044  
+**Version**: 1.0.0