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EfficientNet-B4 Texture Branch for deepfake detection.
Purpose: Capture texture-level artifacts that complement ViT's structural analysis.
Trained on face-aligned crops to focus on skin texture, hair detail, and
boundary artifacts common in AI-generated portraits.
Architecture:
EfficientNet-B4 (pretrained ImageNet) with:
- Frozen early layers (features[0:5])
- Unfrozen late layers (features[5:]) for texture-level fine-tuning
- Custom classifier head: Linear(1792, 512) -> ReLU -> Dropout -> Linear(512, 1) -> Sigmoid
Output: P(AI-generated)
Saves as: models/efficient.pth
"""
import torch.nn as nn
from torchvision.models import efficientnet_b4, EfficientNet_B4_Weights
class EfficientNetTexture(nn.Module):
"""
EfficientNet-B4 fine-tuned for detecting texture artifacts in deepfakes.
Operates on face-aligned 224x224 crops.
Outputs P(AI-generated).
"""
def __init__(self):
super().__init__()
weights = EfficientNet_B4_Weights.IMAGENET1K_V1
base = efficientnet_b4(weights=weights)
# Freeze early feature blocks (low-level features are transferable)
for i, block in enumerate(base.features):
if i < 5:
for p in block.parameters():
p.requires_grad = False
self.features = base.features
self.avgpool = base.avgpool
# EfficientNet-B4 outputs 1792 features
in_features = 1792
self.classifier = nn.Sequential(
nn.Linear(in_features, 512),
nn.ReLU(inplace=True),
nn.Dropout(0.4),
nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def forward(self, x):
"""
Args:
x: (batch, 3, 224, 224) normalized face crop
Returns:
(batch, 1) P(AI-generated)
"""
x = self.features(x)
x = self.avgpool(x)
x = x.flatten(1)
return self.classifier(x)
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