README.txt

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# Metaheuristic-Driven Optimization of Ensemble Deep Learning Model for Image Manipulation Classification- https://github.com/deepbrainscu/ImageManipulation



## 📌 Description
This repository provides the implementation of deep learning models for **multi-manipulation classification** in digital images.  
The framework leverages **SE-ResNet50**, **SE-DenseNet121**, **SE-InceptionV3**, and **ViT-Base/32** architectures to detect various preprocessing manipulations applied to images.  
Models were trained from scratch (no transfer learning) and evaluated on three benchmark datasets.

---

## 📂 Dataset Information
This study utilized **three benchmark datasets** to evaluate the proposed method under diverse image formats and compression characteristics:

- **BOSSBase** (Boroumand & Fridrich, 2018)  
  Format: `.pgm` (uncompressed)  
  Widely adopted in digital image forensics and steganalysis.  
  Publicly available: [https://dde.binghamton.edu/download/](https://dde.binghamton.edu/download/)

- **UCID** (Schaefer & Stich, 2003; Nguyen et al., 2022)  
  Format: `.tif` (uncompressed)  
  Originally developed for research on image compression and retrieval.  
  Accessible at: [https://www.kaggle.com/datasets/flamense160/ucid-dataset/data](https://www.kaggle.com/datasets/flamense160/ucid-dataset/data)

- **ImageNet-Mini10K** (subset of Deng et al., 2009)  
  Format: `.jpeg`  
  Contains randomly selected images from 10 classes of the original ImageNet database.  
  Full ImageNet available at: [https://www.image-net.org/](https://www.image-net.org/)

📏 **Preprocessing**  
- All images were resized to `512×512`.  
- Split: **70% training / 20% testing / 10% validation**.  

### ⚙️ Manipulation Classes
To generate the manipulated image classes, preprocessing operations were applied with MATLAB:

#### 🔹 Compressed-only image class
Double JPEG compression:  
- QF1 ∈ {85, 86, …, 97, 98} (random)  
- QF2 ∈ {75, 85, 95} (fixed)

#### 🔹 Intensity-adjustment class
Gamma correction and histogram equalization:
````matlab
imadjust(I, [low high] = stretchlim(I, 2/100), [], gamma=0.8)
imadjust(I, [low high] = stretchlim(I, 6/100), [], gamma=1.2)
histeq(I)
````

#### 🔹 Low-pass class
Gaussian and average filtering:
````matlab
imfilter(I, fspecial('gaussian', 3, 1), 'symmetric')
imfilter(I, fspecial('gaussian', 5, 1.5), 'symmetric')
imfilter(I, fspecial('average', 3), 'symmetric')
````

#### 🔹 High-pass class
Sharpening filters:
````matlab
imfilter(I, fspecial('unsharp', 0.5), 'symmetric')
imsharpen(I, 'Radius', 1.5, 'Amount', 2)
imsharpen(I, 'Radius', 2, 'Amount', 2)
````

#### 🔹 Denoising class
Wiener and wavelet filtering:
````matlab
wiener2(I, [3 3])
wiener2(I, [5 5])
wdencmp('gbl', I, 'db12', 2, 8, 'h', 'one')
````

---

## 🚀 Usage Instructions

Clone the repository:

````bash
git clone https://github.com/deepbrainscu/ImageManipulation.git
cd ImageManipulation
````

Install dependencies:

````bash
pip install -r requirements.txt
````

Prepare datasets (download from links above, preprocess as described).

Train a model:

````bash
python im_history.py
````

### 📥 Model Weights
Pretrained model weights can be downloaded from the following link:  
👉 [Download Weights (Google Drive)](https://drive.google.com/drive/folders/1peKxjK1ZCMFJ4gq3_6H_SkyVgpsiUEQm?usp=drive_link)

Place the downloaded `.h5` files in the `weights/` directory or specify their path when running evaluation:
Evaluate:

````bash

python predict_genetic_ucid.py --image "D:\\Imhistory\\ImageHistory\\Datasets\\UCID\\Test\\QF=75\\Adjustment\\3_uniformT75_adj1.jpg" --qf 75
````


## 📦 Requirements
- Python 3.9+
- TensorFlow 2.9+
- NumPy
- OpenCV
- scikit-learn
- Matplotlib

---

## 🧪 Methodology
1. Preprocessing and manipulation of datasets using MATLAB functions.  
2. Training deep learning models from scratch (no transfer learning).  
3. Evaluation metrics: Accuracy, Precision, Recall, F1-Score.  
4. Comparison between standard CNNs and SE-enhanced versions, plus Vision Transformers.  

---

## 📖 Citations
If you use this repository, please cite:

```bibtex
@article{karakis2025imagemanipulation,
  author    = {Karakis, Rukiye, Gurkahraman Kali, Unsal Emre, Ates Volkan},
  title     = {Metaheuristic-Driven Optimization of Ensemble Deep Learning Model for Image Manipulation Classification},
  journal   = {PeerJ Computer Science},
  year      = {2025},
  note      = {Under review}
}
```

Additional references:
- He et al., 2016 – ResNet  
- Huang et al., 2017 – DenseNet  
- Szegedy et al., 2016 – InceptionV3  
- Hu et al., 2018 – Squeeze-and-Excitation Networks  
- Dosovitskiy et al., 2021 – Vision Transformers  
- Boroumand & Fridrich, 2018 – BOSSBase  
- Schaefer & Stich, 2003 – UCID  

---

## 📜 License & Contributions
- Licensed under the **MIT License**.  
- Contributions are welcome via pull requests.  
- Parts of SE-ResNet, SE-DenseNet, and SE-Inception implementations were adapted from:  
  [keras-squeeze-excite-network](https://github.com/titu1994/keras-squeeze-excite-network) (MIT License).  

---

## 🙏 Acknowledgments
We thank the authors of open-source repositories that inspired this work.  
Special thanks to the **DeepBrain research group** and **SCU team members** for their support.