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3b237c2 | 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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | import torch
import torch.nn as nn
import timm
from pathlib import Path
import logging
import os
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class EfficientNetDeepFakeDetector(nn.Module):
"""Frame-level EfficientNet-B0 with temporal mean-pooling."""
FEAT_DIM = 1280
def __init__(self, dropout: float = 0.4):
super().__init__()
# Backbone
backbone = timm.create_model(
'efficientnet_b0',
pretrained=False,
num_classes=0,
global_pool='avg'
)
# Freeze BatchNorm layers
for m in backbone.modules():
if isinstance(m, (nn.BatchNorm2d, nn.SyncBatchNorm)):
m.eval()
for p in m.parameters():
p.requires_grad = False
self.backbone = backbone
# Classifier head
self.head = nn.Sequential(
nn.LayerNorm(self.FEAT_DIM),
nn.Dropout(dropout),
nn.Linear(self.FEAT_DIM, 256),
nn.GELU(),
nn.Dropout(dropout * 0.5),
nn.Linear(256, 1)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, T, C, H, W = x.shape
x = x.view(B * T, C, H, W)
feat = self.backbone(x)
feat = feat.view(B, T, self.FEAT_DIM)
feat = feat.mean(dim=1)
logit = self.head(feat).squeeze(-1)
return logit
class DeepFakeModel:
def __init__(self, model_path: str, device: str = "cpu"):
self.device = torch.device(device)
self.model = EfficientNetDeepFakeDetector(dropout=0.4).to(self.device)
self._load_model(model_path)
self.model.eval()
logger.info(f"Model loaded on {self.device}")
def _load_model(self, model_path: str):
"""Load model checkpoint from file"""
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found at {model_path}")
checkpoint = torch.load(model_path, map_location=self.device, weights_only=False)
# Handle different checkpoint formats
if 'model_state_dict' in checkpoint:
self.model.load_state_dict(checkpoint['model_state_dict'])
epoch = checkpoint.get('epoch', 'unknown')
val_f1 = checkpoint.get('val_f1_macro', 'unknown')
logger.info(f"Loaded checkpoint from epoch {epoch} (val_f1={val_f1})")
else:
# If checkpoint is just the state dict
self.model.load_state_dict(checkpoint)
logger.info("Loaded model state dict")
@torch.no_grad()
def predict(self, video_tensor: torch.Tensor, threshold: float = 0.5) -> dict:
"""
Predict if video is real or fake.
Args:
video_tensor: Tensor of shape (T, 3, H, W) or (1, T, 3, H, W)
threshold: Decision threshold (default: 0.5)
Returns:
dict with prediction, confidence, and probabilities
"""
if video_tensor.dim() == 4:
video_tensor = video_tensor.unsqueeze(0)
video_tensor = video_tensor.to(self.device)
logit = self.model(video_tensor)
prob = torch.sigmoid(logit).item()
# prob = P(REAL), because training used label 1=REAL, 0=FAKE
prediction = "REAL" if prob >= threshold else "FAKE"
confidence = prob if prediction == "REAL" else 1 - prob
return {
"prediction": prediction,
"confidence": round(confidence, 4),
"probability_real": round(prob, 4),
"probability_fake": round(1 - prob, 4),
"threshold": threshold
}
@torch.no_grad()
def predict_from_video_path(self, video_path: str, threshold: float = 0.5) -> dict:
"""
Convenience method to predict directly from video file path.
Args:
video_path: Path to video file
threshold: Decision threshold
Returns:
Prediction result dictionary
"""
from .utils import video_to_tensor
video_tensor = video_to_tensor(
video_path,
num_frames=16,
img_size=224
)
return self.predict(video_tensor, threshold) |