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README.md

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+# DeepFake Detection with CNNs & Transfer Learning
+
+This assignment is part of the **CENG 481 - Artificial Neural Networks** course assignment.
+It addresses the task of detecting deepfake content using image-based CNN classification and transfer learning techniques.
+
+---
+
+## 🎯 Objective
+
+- Build an end-to-end image-based deepfake detection pipeline
+- Extract and align 10 frames per video clip
+- Pair each real frame with a corresponding fake variation
+- Train a CNN model using EfficientNetB0 with ImageNet weights
+- Apply regularization, checkpointing, and early stopping for best performance
+- Evaluate using AUC-ROC, accuracy, precision, recall, F1-score
+
+---
+
+## 📦 Dataset
+
+- **Source**: [DFDC Part-34 on Kaggle](https://www.kaggle.com/datasets/greatgamedota/dfdc-part-34)
+- **Metadata**: `metadata34.csv`
+- Each video is represented by 10 frames: `0.jpg`, `30.jpg`, ..., `270.jpg`
+- Fake videos are linked to their originals via metadata
+
+---
+
+## 🧠 Model
+
+- Base: `EfficientNetB0`, pretrained on ImageNet
+- Frozen base trained with custom head; then base unfrozen and fine-tuned
+- Architecture: GlobalAveragePooling2D → Dropout(0.4) → Dense(1, sigmoid)
+- Input size: 224×224×3
+- Optimizer: Adam (`lr=1e-4` frozen, `lr=1e-5` unfrozen)
+- Loss: Binary Crossentropy
+- Metrics: AUC, Accuracy, Precision, Recall, F1
+
+---
+
+## 🏋️ Training
+
+- Balanced dataset from 6784 images (REAL + FAKE)
+- Train/Test split: 79% / 21% (stratified)
+- Batch size: 32
+- Epochs: max 100 (early stopping with patience=8)
+- Model checkpointing enabled (.keras format)
+- TensorBoard used for experiment tracking
+- Platform: Google Colab (GPU)
+
+---
+
+## 🧪 Evaluation (Final Results)
+
+- Accuracy: 0.80
+- AUC-ROC: 0.88
+- Precision: 0.78
+- Recall: 0.82
+- F1-Score: 0.80
+
+---
+
+## 💾 How to Use
+
+```python
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.models import load_model
+import numpy as np
+import cv2
+
+# Load and preprocess image
+def preprocess_image(path):
+    img = cv2.imread(path)
+    img = cv2.resize(img, (224, 224))
+    img = img / 255.0
+    return img.astype(np.float32)
+
+# Download and load model
+model_path = hf_hub_download(repo_id="fc63/deepfake-detection-cnn_v2", filename="best_model.keras")
+model = load_model(model_path)
+
+# Predict
+img = preprocess_image("frame.jpg")
+pred = model.predict(img[np.newaxis, ...])
+
+print("FAKE" if pred[0][0] > 0.5 else "REAL")
+```
+
+---
+
+## 📁 Requirements
+
+```
+tensorflow
+scikit-learn
+pandas
+matplotlib
+opencv-python
+huggingface_hub
+```
+
+---
+
+## 🔗 Repositories
+
+- 🤗 Model: https://huggingface.co/fc63/deepfake-detection-cnn_v2
+- 💻 Codebase: https://github.com/fc63/Deep-Fake-Video-Detection
+
+---
+
+## ⚠️ Ethical Considerations
+
+Deepfake technology poses threats to media trust, privacy, and security. This assignment aims to mitigate misuse by improving detection accuracy while acknowledging dataset limitations and the risk of bias.
+
+---
+
+## 👤 Author
+
+**Furkan Çoban**  
+Çankaya University
+
+---
+
+## 🧑‍🏫 Instructor
+
+This assignment was completed as part of the CENG 481 - Artificial Neural Networks course  
+at Çankaya University under the supervision of **Dr. Nurdan Saran**.