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import gradio as gr
from transformers import pipeline
from PIL import Image, ExifTags, ImageStat, ImageFilter
import numpy as np
# Model utama untuk deteksi AI
detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
# Model tambahan general classifier (backup)
general = pipeline("image-classification", model="google/vit-base-patch16-224")
def analyze_noise(img):
gray = img.convert("L")
arr = np.array(gray)
return np.std(arr) # Standar deviasi → noise
def analyze_blur(img):
gray = img.convert("L")
arr = np.array(gray)
lap = cv2.Laplacian(arr, cv2.CV_64F).var()
return lap # Variansi Laplacian → blur
def metadata_score(img):
try:
exif = img._getexif()
if exif is None:
return 1 # Tidak ada metadata → kemungkinan AI
for tag, value in exif.items():
decoded = ExifTags.TAGS.get(tag, tag)
if decoded == "Make" or decoded == "Model":
return 0 # Ada kamera → kemungkinan asli
return 1
except:
return 1
def detect_image(img: Image.Image):
try:
# Prediksi AI-detector
result1 = detector(img)
label1 = result1[0]['label']
conf1 = result1[0]['score'] # 0-1
# Prediksi general model
result2 = general(img)
label2 = result2[0]['label']
conf2 = result2[0]['score']
# Analisis blur & noise
noise = analyze_noise(img)
try:
import cv2
blur = analyze_blur(img)
except:
blur = 0
# Metadata
meta = metadata_score(img)
# Weighted skor akhir (lebih sensitif terhadap AI photorealistic)
ai_score = conf1 * 0.6 + meta * 0.2 + (1 - min(noise/100,1)) * 0.1 + (1 - min(blur/1000,1)) * 0.1
ai_score = min(max(ai_score, 0), 1) # Clamp 0-1
human_score = 1 - ai_score
ai_percent = round(ai_score * 100, 2)
human_percent = round(human_score * 100, 2)
output = f"""
### Hasil Deteksi:
🖼️ Gambar ini {ai_percent}% AI / {human_percent}% Asli
**Model AI-detector:** {label1} ({round(conf1*100,2)}%)
**Model General (ViT):** {label2} ({round(conf2*100,2)}%)
**Blur Score:** {round(blur,2)}
**Noise Score:** {round(noise,2)}
**Metadata Kamera:** {"Ada" if meta==0 else "Tidak Ada"}
"""
return output
except Exception as e:
return f"Terjadi error: {str(e)}"
iface = gr.Interface(
fn=detect_image,
inputs=gr.Image(type="pil"),
outputs="markdown",
title="Hybrid AI vs Foto Asli Detector",
description="Upload foto untuk mendeteksi persentase AI vs foto asli. Lebih sensitif terhadap AI photorealistic."
)
if __name__ == "__main__":
iface.launch()
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