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a972d65 717222a a972d65 717222a a972d65 717222a a972d65 717222a a972d65 717222a a972d65 717222a a972d65 717222a a972d65 | 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 | import torch
from torchvision import transforms
from PIL import Image, ImageOps
import tempfile
import os
from detector_config import (
ALLOW_LOCAL_MODEL_FALLBACK,
IMAGE_DETECTOR_BACKEND,
IMAGE_FAKE_THRESHOLD,
IMAGE_UNCERTAIN_MARGIN,
)
from model_loader import get_image_model
def build_image_insight(result, confidence, fake_score, real_score):
margin = abs(fake_score - real_score) * 100
if confidence >= 90:
certainty = "High"
elif confidence >= 70:
certainty = "Moderate"
else:
certainty = "Low"
if result == "Uncertain":
summary = "The detector did not find a large enough gap between fake and real evidence."
elif certainty == "Low":
summary = "The model is not strongly confident. Treat this as a signal, not a final judgement."
elif result == "Fake":
summary = "The image contains patterns the model associates with manipulated or synthetic content."
else:
summary = "The image looks closer to authentic content based on the model's learned patterns."
return {
"certainty": certainty,
"summary": summary,
"scores": {
"fake": round(fake_score * 100, 2),
"real": round(real_score * 100, 2),
},
"metrics": {
"confidence": round(confidence, 2),
"score_gap": round(margin, 2),
"uncertainty": round(100 - confidence, 2),
"consistency": 100,
},
"risk_level": "High" if result == "Fake" and confidence >= 80 else "Medium" if result == "Fake" else "Low",
}
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]
)
])
def detect_deepfake(file):
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp:
file.save(temp.name)
path = temp.name
try:
image = ImageOps.exif_transpose(Image.open(path)).convert("RGB")
if IMAGE_DETECTOR_BACKEND == "huggingface":
try:
from hf_detectors import get_hf_image_detector
result = get_hf_image_detector().predict(
image,
threshold=IMAGE_FAKE_THRESHOLD,
uncertain_margin=IMAGE_UNCERTAIN_MARGIN,
)
result["insight"] = build_image_insight(
result["result"],
result["confidence"],
result["fake_score"] / 100,
result["real_score"] / 100,
)
return result
except Exception as error:
if not ALLOW_LOCAL_MODEL_FALLBACK:
return {"error": f"Hugging Face image detector failed: {error}"}
img = transform(image).unsqueeze(0)
with torch.no_grad():
output = get_image_model()(img)
fake_score = torch.sigmoid(output).item()
real_score = 1 - fake_score
THRESHOLD = IMAGE_FAKE_THRESHOLD
if fake_score > THRESHOLD:
result = "Fake"
confidence = fake_score
else:
result = "Real"
confidence = real_score
return {
"result": result,
"confidence": round(confidence * 100, 2),
"fake_score": round(fake_score * 100, 2),
"real_score": round(real_score * 100, 2),
"raw_probability": round(fake_score, 6),
"insight": build_image_insight(result, confidence * 100, fake_score, real_score),
}
except Exception as e:
return {"error": str(e)}
finally:
if os.path.exists(path):
os.remove(path)
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