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# backend/model.py
import cv2
import numpy as np
from transformers import pipeline
from PIL import Image, ImageOps
import torch
import io
import base64
class DualModelDetector:
def __init__(self):
print("⏳ Loading Models...")
device = 0 if torch.cuda.is_available() else -1
# MODEL 1: GenAI Detector
print(" 1. Loading GenAI Detector (v2.0)...")
self.genai_pipe = pipeline("image-classification", model="prithivMLmods/AI-vs-Deepfake-vs-Real-v2.0", device=device)
# MODEL 2: Face Deepfake Detector
print(" 2. Loading Face Deepfake Detector (v2)...")
self.face_pipe = pipeline("image-classification", model="prithivMLmods/Deep-Fake-Detector-v2-Model", device=device)
self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
print("✅ System Ready: Visual Debug Mode Active")
def img_to_base64(self, img):
"""Converts a PIL Image to a Base64 string for the frontend"""
buffered = io.BytesIO()
img.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def predict(self, image: Image.Image):
try:
if image.mode != "RGB":
image = image.convert("RGB")
# --- PHASE 1: GENAI DETECTION ---
genai_results = self.genai_pipe(image)
genai_top = genai_results[0]
genai_score = genai_top['score']
is_ai_art = "artificial" in genai_top['label'].lower()
genai_label = "Real Image"
if is_ai_art and genai_score > 0.6:
genai_label = "AI Generated Art"
genai_data = {
"is_detected": is_ai_art,
"confidence": genai_score,
"label": genai_label
}
# --- PHASE 2: FACE DETECTION ---
open_cv_image = np.array(image)
open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_RGB2BGR)
gray = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2GRAY)
faces = self.face_cascade.detectMultiScale(gray, 1.1, 4)
deepfake_data = {
"face_found": False,
"is_detected": False,
"confidence": 0.0,
"label": "No Face Found"
}
# Default to full image if no face (so we can still see what it saw)
target_face_image = image
if len(faces) > 0:
deepfake_data["face_found"] = True
sorted_faces = sorted(faces, key=lambda b: b[2] * b[3], reverse=True)
x, y, w, h = sorted_faces[0]
# Ratio Check logic
image_area = image.width * image.height
face_area = w * h
face_ratio = face_area / image_area
if face_ratio > 0.20:
# Case A: Large Face (Portrait) -> Use Full Image
target_face_image = image
else:
# Case B: Small Face -> Crop it
max_dim = max(w, h)
margin = int(max_dim * 0.6)
center_x = x + w // 2
center_y = y + h // 2
left = max(0, center_x - (max_dim + margin) // 2)
top = max(0, center_y - (max_dim + margin) // 2)
right = min(image.width, center_x + (max_dim + margin) // 2)
bottom = min(image.height, center_y + (max_dim + margin) // 2)
target_face_image = image.crop((left, top, right, bottom))
# Preprocess (Pad to Square)
target_face_image = ImageOps.pad(target_face_image, (224, 224), color="black")
# --- GENERATE DEBUG IMAGE ---
# This is the exact pixel data the AI is analyzing
debug_b64 = self.img_to_base64(target_face_image)
# Run Deepfake Model
face_results = self.face_pipe(target_face_image)
face_top = face_results[0]
is_deepfake = "fake" in face_top['label'].lower() or "deepfake" in face_top['label'].lower()
deepfake_score = face_top['score']
SAFE_THRESHOLD = 0.55
if is_deepfake and deepfake_score < SAFE_THRESHOLD:
is_deepfake = False
deepfake_score = 0.0
deepfake_data.update({
"is_detected": is_deepfake,
"confidence": deepfake_score,
"label": "Deepfake Face" if is_deepfake else "Real Face"
})
return {
"genai_analysis": genai_data,
"deepfake_analysis": deepfake_data,
"final_verdict": self._get_verdict(genai_data, deepfake_data),
"debug_image": debug_b64 # <--- SENDING IMAGE BACK
}
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
return {"error": str(e)}
def _get_verdict(self, genai, deepfake):
if deepfake['face_found'] and deepfake['is_detected']:
return "Deepfake Detected"
if genai['is_detected']:
return "AI Generated Image"
return "Real Image" |