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model.py

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+
+import torch
+import torch.nn as nn
+import timm
+
+class SRMLayer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        f1 = torch.tensor([
+            [ 0,  0,  0,  0,  0],
+            [ 0, -1,  2, -1,  0],
+            [ 0,  2, -4,  2,  0],
+            [ 0, -1,  2, -1,  0],
+            [ 0,  0,  0,  0,  0]
+        ], dtype=torch.float32)
+        f2 = torch.tensor([
+            [-1,  2, -2,  2, -1],
+            [ 2, -6,  8, -6,  2],
+            [-2,  8,-12,  8, -2],
+            [ 2, -6,  8, -6,  2],
+            [-1,  2, -2,  2, -1]
+        ], dtype=torch.float32)
+        f3 = torch.tensor([
+            [ 0,  0,  0,  0,  0],
+            [ 0,  0,  0,  0,  0],
+            [ 0,  1, -2,  1,  0],
+            [ 0,  0,  0,  0,  0],
+            [ 0,  0,  0,  0,  0]
+        ], dtype=torch.float32)
+        filters = torch.stack([f1, f2, f3]).unsqueeze(1)
+        self.conv = nn.Conv2d(1, 3, kernel_size=5, padding=2, bias=False)
+        self.conv.weight.data = filters
+        self.conv.weight.requires_grad = False
+
+    def forward(self, x):
+        gray = 0.299*x[:,0:1] + 0.587*x[:,1:2] + 0.114*x[:,2:3]
+        return self.conv(gray)
+
+class HybridDeepfakeDetector(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.spatial = timm.create_model(
+            "efficientnet_b4", pretrained=False, num_classes=0)
+        self.srm = SRMLayer()
+        self.frequency = timm.create_model(
+            "xception", pretrained=False, num_classes=0, in_chans=3)
+        self.classifier = nn.Sequential(
+            nn.Linear(3840, 256),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(256, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        spatial_features = self.spatial(x)
+        noise_maps = self.srm(x)
+        freq_features = self.frequency(noise_maps)
+        combined = torch.cat([spatial_features, freq_features], dim=1)
+        return self.classifier(combined)