Update README.md

README.md

@@ -4,4 +4,180 @@ license: mit
 
 # 🖼️ Image Forgery Detection Model
 
-This repository contains a deep learning model that detects whether an image is REAL or FAKE..
+license: mit
+pipeline_tag: image-classification
+library_name: tensorflow
+tags:
+- image-forgery-detection
+- ai-generated-images
+- deepfake-detection
+- fake-image-detection
+- image-forensics
+- computer-vision
+- mobilenetv2
+- tensorflow
+- keras
+---
+
+# 🕵️ AI Image Forgery Detector (Real vs Fake)
+
+This model detects whether an image is **REAL (camera-captured)** or **FAKE (AI-generated / manipulated)**.
+
+It is a **fine-tuned MobileNetV2 model** trained using TensorFlow/Keras, designed for **AI image forgery detection**, including synthetic and deepfake-style images.
+
+---
+
+## 🔍 What This Model Can Do
+
+✔ Detect AI-generated images  
+✔ Detect manipulated or synthetic content  
+✔ Works on faces, objects, landscapes, and artworks  
+✔ Outputs **prediction + confidence score**
+
+### Class Labels
+- **0 → Fake**
+- **1 → Real**
+
+---
+
+## 🧠 Model Architecture
+
+- **Base model:** MobileNetV2 (ImageNet pretrained)
+- **Framework:** TensorFlow / Keras
+- **Input size:** 224 × 224 RGB image
+- **Output:** Sigmoid probability
+- **Task:** Binary image classification
+
+Additional layers:
+- Global Average Pooling
+- Dense (ReLU)
+- Dropout (regularization)
+- Sigmoid output layer
+
+---
+
+## 📊 Evaluation Results
+
+The model was evaluated using **accuracy, precision, recall, and F1-score**, with threshold optimization based on **macro F1-score** to handle class imbalance.
+
+### Best Validation Results
+- **Accuracy:** ~87%
+- **Fake class:** High precision
+- **Real class:** High recall
+- **Threshold:** Optimized (~0.65)
+
+> Results may vary depending on image quality and source.
+
+---
+
+## 🖼️ Example Predictions
+
+### ✅ Real Image (Correct Classification)
+![Real Image Example](examples/real_example.jpg)
+
+**Prediction:** REAL  
+**Confidence:** High
+
+---
+
+### ❌ AI-Generated Image (Correct Classification)
+![Fake Image Example](examples/fake_example.jpg)
+
+**Prediction:** FAKE  
+**Confidence:** High
+
+---
+
+## 🚀 How to Use This Model
+
+```python
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+IMG_SIZE = 224
+
+# Load model
+model = tf.keras.models.load_model("forgery_model.keras")
+
+# Load and preprocess image
+img = Image.open("test.jpg").convert("RGB")
+img = img.resize((IMG_SIZE, IMG_SIZE))
+img = np.array(img) / 255.0
+img = np.expand_dims(img, axis=0)
+
+# Predict
+probability = model.predict(img)[0][0]
+
+label = "REAL" if probability > 0.65 else "FAKE"
+print("Prediction:", label)
+print("Confidence:", probability)
+🎯 Intended Use
+This model is suitable for:
+
+AI-generated image detection
+
+Image forgery analysis
+
+Academic projects
+
+Research and experimentation
+
+Educational demonstrations
+
+❌ Not recommended for legal, forensic, or high-risk decision-making systems.
+
+⚠️ Limitations
+Extremely realistic AI images may still fool the model
+
+Performance can drop on:
+
+Heavy compression
+
+Very low resolution
+
+Extreme image edits
+
+Model predictions are probabilistic, not absolute truth
+
+Always validate outputs before real-world use.
+
+🧪 Training Details
+Base: MobileNetV2 (ImageNet weights)
+
+Optimizer: Adam (low learning rate)
+
+Loss: Binary Crossentropy
+
+Data augmentation applied
+
+Class imbalance handled using class weights
+
+Threshold tuning performed after training
+
+🧭 Ethical Considerations
+Predictions can be incorrect
+
+False positives / negatives are possible
+
+Use responsibly and transparently
+
+Do not rely solely on this model for critical decisions
+
+📜 License
+This model is released under the MIT License.
+
+👤 Author
+Kumaran
+Hugging Face: kumaran-0188
+
+Built as part of an AI image forgery detection project using TensorFlow.
+
+🙌 Acknowledgements
+TensorFlow & Keras
+
+MobileNetV2
+
+AI-generated image datasets (CIFAKE-style)
+
+Hugging Face ecosystem