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3b2dd6d | 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 | import os
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Step 2: Load the AI Classifier (Siglip2)
# This model is specifically fine-tuned to distinguish between Real and AI-generated images
model_name = "prithivMLmods/Deepfake-Detect-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Step 3: Perception Logic
def analyze_kyc_image(image):
if image is None:
return "ERROR: No image provided."
try:
# Convert to RGB if needed (handles RGBA or grayscale)
if not isinstance(image, Image.Image):
image = Image.fromarray(image).convert("RGB")
# Pre-process the image for the Siglip2 architecture
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Convert logits to probabilities
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
# Get labels ('Fake' vs 'Real')
labels = model.config.id2label
# Identify the top prediction
top_idx = 0 if probs[0] > probs[1] else 1
verdict = labels[top_idx]
confidence = probs[top_idx]
# Step 4: Human-Readable Reporting
if verdict.lower() == "fake":
return f"🚨 ALERT: AI MANIPULATION DETECTED!\n\n📊 Fake Confidence: {confidence:.1%}\n(Model suggests this image is AI-generated or digitally altered)"
else:
return f"✅ SUCCESS: GENUINE PHOTOGRAPH VERIFIED.\n\n📊 Real Confidence: {confidence:.1%}\n(No signs of deepfake synthesis detected)"
except Exception as e:
return f"ERROR analyzing image: {str(e)}"
# Step 5: Build the Sentinel Interface (Gradio)
with gr.Blocks(title="IOB Sentinel: Image KYC Verifier") as demo:
gr.Markdown("# IOB Sentinel: Image KYC Deepfake Detector")
gr.Markdown("Uses Siglip2-Base to identify AI-generated portraits and digital face-swaps.")
with gr.Tabs():
with gr.TabItem("Upload ID Photo"):
with gr.Row():
with gr.Column():
img_input = gr.Image(type="numpy", label="Upload Identity Document / Photo")
submit_btn = gr.Button("Analyze Image", variant="primary")
with gr.Column():
text_output = gr.Textbox(label="Verification Result", interactive=False)
submit_btn.click(fn=analyze_kyc_image, inputs=img_input, outputs=text_output)
with gr.TabItem("Live Capture"):
with gr.Row():
with gr.Column():
webcam_input = gr.Image(sources=["webcam"], type="numpy", label="Capture Live Photo")
webcam_btn = gr.Button("Analyze Capture", variant="primary")
with gr.Column():
webcam_output = gr.Textbox(label="Verification Result", interactive=False)
webcam_btn.click(fn=analyze_kyc_image, inputs=webcam_input, outputs=webcam_output)
if __name__ == "__main__":
demo.launch()
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