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from PIL import Image
from schemas.vision_schemas import FakeFaceDetector
import cv2
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, BatchNormalization, MaxPooling2D, Flatten, Dropout, Dense
from tensorflow.keras.preprocessing.image import img_to_array
from PIL import Image
class Meso4FakeFaceDetector(FakeFaceDetector):
"""
Deepfake face detector using Meso4 models trained on DF and F2F datasets.
Combines both models' predictions for a more robust detection.
"""
def __init__(self,
df_model_path: str = "models/Meso4_DF.h5",
f2f_model_path: str = "models/Meso4_F2F.h5",
input_shape=(256, 256, 3)):
"""
Initialize and load both pretrained models.
Args:
df_model_path (str): Path to Meso4 model trained on DeepFake dataset.
f2f_model_path (str): Path to Meso4 model trained on Face2Face dataset.
input_shape (tuple): Expected input shape for the models.
"""
self.df_model_path = df_model_path
self.f2f_model_path = f2f_model_path
self.input_shape = input_shape
# Build both models
self.model_df = self._build_meso4()
self.model_f2f = self._build_meso4()
# Load pretrained weights
self.model_df.load_weights(self.df_model_path)
self.model_f2f.load_weights(self.f2f_model_path)
# ------------------ Internal Model Builder ------------------
def _build_meso4(self):
"""Build the Meso4 CNN model architecture."""
model = Sequential()
model.add(Conv2D(8, (3, 3), activation='relu', padding='same', input_shape=self.input_shape))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
model.add(Conv2D(8, (5, 5), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
model.add(Conv2D(16, (5, 5), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
model.add(Conv2D(16, (5, 5), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(4, 4), strides=4))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
# ------------------ Image Preprocessing ------------------
def _preprocess_image(self, image: Image.Image) -> np.ndarray:
"""
Preprocess a PIL image for prediction.
Args:
image (PIL.Image): Input face image.
Returns:
np.ndarray: Preprocessed image ready for model input.
"""
img = image.convert("RGB")
img = img.resize((256, 256))
img = img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0)
return img
# ------------------ Detection ------------------
def detect(self, image: Image.Image, threshold: float = 0.5, verbose=True) -> bool:
"""
Detect whether an input face image is fake or real.
Args:
image (PIL.Image): Input image of a face.
threshold (float): Decision threshold (default 0.5).
Returns:
bool: True if fake, False if real.
"""
img = self._preprocess_image(image)
# Get individual predictions
pred_df = float(self.model_df.predict(img, verbose=0)[0][0])
pred_f2f = float(self.model_f2f.predict(img, verbose=0)[0][0])
# Combine predictions (average)
combined_pred = (pred_df + pred_f2f) / 2.0
if verbose:
# Print detailed info
print(f"🔍 Meso4_DF Prediction: {pred_df:.4f}")
print(f"🔍 Meso4_F2F Prediction: {pred_f2f:.4f}")
print(f"⚖️ Combined Score: {combined_pred:.4f}")
# Determine if fake
is_fake = combined_pred >= threshold
if is_fake:
print("🔴 Deepfake detected.")
else:
print("🟢 Real face detected.")
return is_fake
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