Update README.md

README.md

@@ -1,3 +1,115 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- keras/xception_41_imagenet
+---
+# Model Summary
+
+This model is designed for detecting deepfake content in images and video frames. It uses a lightweight Convolutional Neural Network (CNN) trained on the **FaceForensics++ dataset**, focusing on high-resolution face manipulations (c23 compression). The model classifies whether a face in an input image is **real or fake**.
+
+* Architecture: CNN-based binary classifier
+* Input: Aligned and cropped face images (224x224 RGB)
+* Output: Real or Fake label with confidence
+* Accuracy: \~92% on unseen FaceForensics++ test set
+
+## Usage
+
+```python
+from keras.models import load_model
+import cv2
+import numpy as np
+
+model = load_model('deepfake_cnn_model.h5')
+
+def preprocess(img_path):
+    img = cv2.imread(img_path)
+    img = cv2.resize(img, (224, 224))
+    img = img / 255.0
+    return np.expand_dims(img, axis=0)
+
+input_img = preprocess('test_face.jpg')
+pred = model.predict(input_img)
+print("Fake" if pred[0][0] > 0.5 else "Real")
+```
+
+**Input shape**: `(1, 224, 224, 3)`
+**Output**: Probability of being fake
+
+⚠️ *Fails with very low-resolution images or occluded faces.*
+
+## System
+
+This model is **standalone**, usable in any face verification system or deepfake detection pipeline. Inputs should be properly aligned face crops. Output can be integrated into moderation systems or alerts.
+
+**Dependencies**: Keras/TensorFlow, OpenCV for preprocessing
+
+## Implementation requirements
+
+* Trained on Google Colab with a single NVIDIA T4 GPU
+* Training time: \~6 hours over 30 epochs
+* Model inference: <50ms per image
+* Memory requirement: \~150MB RAM at inference
+
+# Model Characteristics
+
+## Model initialization
+
+The model was **trained from scratch** using CNN layers, ReLU activations, dropout, and batch normalization.
+
+## Model stats
+
+* Size: \~10MB
+* Layers: \~8 convolutional layers + dense head
+* Inference latency: \~40ms on GPU, \~200ms on CPU
+
+## Other details
+
+* Not pruned or quantized
+* No use of differential privacy during training
+
+# Data Overview
+
+## Training data
+
+* Dataset: FaceForensics++ (c23 compression level)
+* Preprocessing: face alignment (using Dlib), resize to 224x224, normalization
+* Augmentations: horizontal flip, brightness variation
+
+## Demographic groups
+
+The dataset contains celebrity faces scraped from YouTube. It includes a mix of ethnicities and genders, but **not balanced or labeled** explicitly by demographic.
+
+## Evaluation data
+
+* Train/Val/Test: 70% / 15% / 15%
+* The test set includes unseen identities and manipulations (Deepfakes, FaceSwap, NeuralTextures)
+
+# Evaluation Results
+
+## Summary
+
+* Accuracy: \~92%
+* F1 Score: 0.91
+* ROC-AUC: 0.95
+
+## Subgroup evaluation results
+
+No explicit subgroup evaluation was conducted, but performance dropped slightly on:
+
+* Low-light images
+* Images with occlusions (masks, hands)
+
+## Fairness
+
+No explicit fairness metrics were applied due to lack of demographic labels. However, output bias may exist due to uneven representation in training data.
+
+## Usage limitations
+
+* Struggles on low-res or occluded faces
+* Doesn’t work on audio-based or voice deepfakes
+* Requires good lighting and clear facial visibility
+* Not suitable for legal or forensics-grade use cases without further testing
+
+## Ethics
+
+This model is intended for **educational and research purposes only**. It should not be used to make real-world judgments (legal, political, etc.) without human oversight. Deepfake detection systems must be transparent about their limitations and avoid misuse in surveillance or personal targeting.