Update README.md

README.md

@@ -1,20 +1,41 @@
-## Deepfake Detection Model
-
+license: mit # Or apache-2.0, gpl-3.0, etc. Choose the license that applies to your project.
+tags:
+- deepfake-detection
+- video-classification
+- computer-vision
+- xception
+- lstm
+model-index:
+- name: Deepfake Detection Model
+  results:
+  - task:
+      type: video-classification
+      name: Video Classification
+    dataset:
+      name: Your_Dataset_Name # Replace with the actual dataset you trained on (e.g., FaceForensics++, Celeb-DF)
+      type: image-folder
+      split: validation # Or test, or train
+    metrics:
+      - type: accuracy
+        value: 0.95 # Replace with your model's actual accuracy
+        name: Accuracy
+      - type: f1 # Add other relevant metrics like F1-score, precision, recall
+        value: 0.94 # Replace with your model's actual F1 score
+        name: F1 Score
+---
+# Deepfake Detection Model
 This repository contains a deepfake detection model built using a combination of a pre-trained Xception network and an LSTM layer. The model is designed to classify videos as either "Real" or "Fake" by analyzing sequences of facial frames extracted from the video.
 
 ### Model Architecture
 
 The model architecture consists of the following components:
 
-1.  **Input Layer**: Takes a sequence of `TIME_STEPS` frames, each resized to `299x299` pixels with 3 color channels. The input shape is `(batch_size, TIME_STEPS, HEIGHT, WIDTH, 3)`.
-
-2.  **TimeDistributed Xception**: A pre-trained Xception network (trained on ImageNet) is applied to each frame independently using a `TimeDistributed` wrapper. The `include_top` is set to `False`, and `pooling` is set to `'avg'`, effectively using the Xception network as a feature extractor for each frame. This produces a sequence of feature vectors, one for each frame.
-
-3.  **LSTM Layer**: The sequence of feature vectors from the `TimeDistributed Xception` layer is fed into an LSTM (Long Short-Term Memory) layer with `256` hidden units. The LSTM layer is capable of learning temporal dependencies between frames, which is crucial for deepfake detection.
-
-4.  **Dropout Layer**: A `Dropout` layer with a rate of `0.5` is applied after the LSTM layer to prevent overfitting.
+1.  **Input**: Accepts a sequence of `TIME_STEPS` frames, each resized to `299x299` pixels.
+2.  **Feature Extraction**: A **TimeDistributed Xception network** processes each frame, extracting key features.
+3.  **Temporal Learning**: An **LSTM layer** with `256` units learns temporal dependencies between these extracted frame features.
+4.  **Regularization**: A **Dropout layer** (`0.5` rate) prevents overfitting.
+5.  **Output**: A **Dense layer** with `softmax` activation predicts probabilities for "Real" and "Fake" classes.
 
-5.  **Output Layer**: A `Dense` layer with `2` units and a `softmax` activation function outputs the probabilities for the two classes: "Real" and "Fake".
 
 ### How to Use
 
@@ -36,19 +57,47 @@ model = build_model() # Architecture defined in the `build_model` function
 model.load_weights(model_path)
 ```
 
-#### 3\. Face Extraction and Preprocessing
+#### 3\. Model Definition
+
+The `build_model` function defines the architecture:
+
+```python
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+# Global parameters for model input shape (ensure these are defined before calling build_model)
+# Example:
+# TIME_STEPS = 30
+# HEIGHT = 299
+# WIDTH = 299
 
-The `extract_faces_from_video` function processes a given video file:
+def build_model(lstm_hidden_size=256, num_classes=2, dropout_rate=0.5):
+    # Input shape: (batch_size, TIME_STEPS, HEIGHT, WIDTH, 3)
+    inputs = layers.Input(shape=(TIME_STEPS, HEIGHT, WIDTH, 3))
 
-  * It uses the MTCNN (Multi-task Cascaded Convolutional Networks) for robust face detection in each frame.
-  * It samples `TIME_STEPS` frames from the video.
-  * For each sampled frame, it detects the primary face, extracts it, and resizes it to `299x299` pixels.
-  * The extracted face images are then preprocessed using `preprocess_input` from `tensorflow.keras.applications.xception`, which scales pixel values to the range expected by the Xception model.
-  * If no face is detected in a frame, a black image of the same dimensions is used as a placeholder.
-  * The function ensures that exactly `TIME_STEPS` frames are returned, padding with the last available frame or black images if necessary.
+    # TimeDistributed layer to apply the base model to each frame
+    base_model = keras.applications.Xception(weights='imagenet', include_top=False, pooling='avg')
+    # For inference, we don't need to set trainable, but if you plan to retrain, you can set accordingly
+    # base_model.trainable = False
 
-<!-- end list -->
+    # Apply TimeDistributed wrapper
+    x = layers.TimeDistributed(base_model)(inputs)
+    # x shape: (batch_size, TIME_STEPS, 2048)
 
+    # LSTM layer
+    x = layers.LSTM(lstm_hidden_size)(x)
+
+    x = layers.Dropout(dropout_rate)(x)
+    outputs = layers.Dense(num_classes, activation='softmax')(x)
+
+    model = keras.Model(inputs, outputs)
+    return model
+```
+
+#### 3\. Extract Faces
+
+Use the extract_faces_from_video function to get preprocessed face frames from your video. This function handles face detection (using MTCNN), resizing, and preprocessing.
 ```python
 from mtcnn import MTCNN
 import cv2
@@ -57,10 +106,12 @@ from PIL import Image
 from tensorflow.keras.applications.xception import preprocess_input
 
 def extract_faces_from_video(video_path, num_frames=30):
-    # ... (function implementation as provided in prediction.ipynb)
+    # ... (function implementation to extract and preprocess faces)
     pass
 
-video_path = '/content/drive/MyDrive/Dataset DDM/FF++/manipulated_sequences/FaceShifter/raw/videos/724_725.mp4'
+# Ensure TIME_STEPS is defined, as it's used by extract_faces_from_video
+# TIME_STEPS = 30
+video_path = 'path/to/your/video.mp4' # Replace with your video
 video_array = extract_faces_from_video(video_path, num_frames=TIME_STEPS)
 ```
 
@@ -78,14 +129,4 @@ print(f"Predicted Class: {class_names[predicted_class]}")
 print(f"Class Probabilities: Real: {probabilities[0]:.4f}, Fake: {probabilities[1]:.4f}")
 ```
 
-### Parameters
-
-  * `TIME_STEPS`: Number of frames to extract from each video (default: `30`).
-  * `HEIGHT`, `WIDTH`: Dimensions to which each extracted face image is resized (default: `299, 299`).
-  * `lstm_hidden_size`: Number of hidden units in the LSTM layer (default: `256`).
-  * `dropout_rate`: Dropout rate applied after the LSTM layer (default: `0.5`).
-  * `num_classes`: Number of output classes (default: `2` for "Real" and "Fake").
-
-### Development Environment
-
-The provided code snippet is written in Python and utilizes `tensorflow` (Keras API), `opencv-python`, `numpy`, `mtcnn`, and `Pillow`. It is designed to be run in an environment with these libraries installed. The paths suggest it was developed using Google Drive, potentially within a Colab environment.
+<!-- end list -->