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911a5bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import torch
import torch.nn as nn
import torchvision.models as models
from torchvision.models import ResNet18_Weights
class DeepfakeDetector(nn.Module):
def __init__(self, hidden_dim=256, num_layers=2, num_classes=2, dropout=0.5):
super().__init__()
# Load pretrained ResNet18
resnet = models.resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)
# Remove final classification layer to use as feature extractor
self.resnet = nn.Sequential(*list(resnet.children())[:-1])
# Freeze early layers for transfer learning so it remains stable
for layer in list(self.resnet.children())[:6]:
for param in layer.parameters():
param.requires_grad = False
# Bi-LSTM for temporal modeling across frames
self.lstm = nn.LSTM(
input_size=512,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
dropout=dropout,
)
self.dropout = nn.Dropout(dropout)
# Final classifier
self.fc = nn.Linear(hidden_dim * 2, num_classes)
def forward(self, x):
"""
Input shape: (B, T, C, H, W) i.e. (Batch, Time, Channels, Height, Width)
"""
B, T, C, H, W = x.shape
# Flatten temporal dimension for CNN
x = x.view(B * T, C, H, W)
# Extract spatial features
x = self.resnet(x) # (B*T, 512, 1, 1)
x = x.view(B, T, 512)
# Temporal modeling
lstm_out, _ = self.lstm(x)
# Aggregate temporal features (mean pooling)
x = torch.mean(lstm_out, dim=1)
x = self.dropout(x)
return self.fc(x) |