AI IMAGE AND SOURCE DETECTOR

AI IMAGE DETECTOR

Detects if an image is real or AI-generated.
Uses Vision Transformer (ViT) architecture.
High accuracy for various image types.

AI SOURCE DETECTOR

Identifies the source of AI-generated images.
Supports: Midjourney, DALL-E, Stable Diffusion.
Provides confidence scores.

USAGE

Python Code Example for AI Image Detector

from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# Load model and processor
processor = ViTImageProcessor.from_pretrained("yaya36095/ai-image-detector")
model = ViTForImageClassification.from_pretrained("yaya36095/ai-image-detector")

def detect_image(image_path):
    image = Image.open(image_path)
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    predictions = outputs.logits.softmax(dim=-1)
    prediction_id = torch.argmax(predictions).item()
    confidence = predictions[0][prediction_id].item() * 100
    result = "AI Generated" if prediction_id == 1 else "Real Image"
    return result, confidence

# Example usage
# result, confidence = detect_image("path/to/image.jpg")
# print(f"Result: {result} (Confidence: {confidence:.2f}%)")

Python Code Example for AI Source Detector

from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# Load model and processor
processor = ViTImageProcessor.from_pretrained("yaya36095/ai-source-detector")
model = ViTForImageClassification.from_pretrained("yaya36095/ai-source-detector")

def detect_source(image_path):
    image = Image.open(image_path)
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    predictions = outputs.logits.softmax(dim=-1)
    prediction_id = torch.argmax(predictions).item()
    confidence = predictions[0][prediction_id].item() * 100
    sources = ["Real Image", "Midjourney", "DALL-E", "Stable Diffusion"]
    result = sources[prediction_id]
    return result, confidence

# Example usage
# result, confidence = detect_source("path/to/image.jpg")
# print(f"Source: {result} (Confidence: {confidence:.2f}%)")

Combined Analysis Code Example

def analyze_image(image_path):
    """
    Analyzes an image to detect if it is AI-generated and its source.

    Args:
        image_path: Path to the image file

    Returns:
        dict: Results containing detection and source information
    """
    # Load models
    detector = ViTForImageClassification.from_pretrained("yaya36095/ai-image-detector")
    source_detector = ViTForImageClassification.from_pretrained("yaya36095/ai-source-detector")
    processor = ViTImageProcessor.from_pretrained("yaya36095/ai-image-detector")
    
    # Open and process image
    image = Image.open(image_path)
    inputs = processor(images=image, return_tensors="pt")
    
    # Get AI detection result
    with torch.no_grad():
        outputs = detector(**inputs)
        predictions = outputs.logits.softmax(dim=-1)
    
    is_ai = torch.argmax(predictions).item() == 1
    ai_confidence = predictions[0][1].item() * 100
    
    # Get source detection if it is AI
    source = "Not AI Generated"
    source_confidence = 0
    
    if is_ai:
        with torch.no_grad():
            outputs = source_detector(**inputs)
            predictions = outputs.logits.softmax(dim=-1)
        
        source_id = torch.argmax(predictions).item()
        sources = ["Real Image", "Midjourney", "DALL-E", "Stable Diffusion"]
        source = sources[source_id]
        source_confidence = predictions[0][source_id].item() * 100
    
    return {
        "is_ai": is_ai,
        "ai_confidence": ai_confidence,
        "source": source,
        "source_confidence": source_confidence
    }

# Example usage:
# result = analyze_image("path/to/image.jpg")
# print(f"AI Generated: {result['is_ai']}")
# print(f"AI Confidence: {result['ai_confidence']:.2f}%")
# print(f"Source: {result['source']}")
# print(f"Source Confidence: {result['source_confidence']:.2f}%")

FEATURES

Supports all image formats.
Automatic image resizing.
Confidence scores for predictions.
Combined analysis (AI detection + Source).

LIMITATIONS

Best with clear, high-quality images.
May vary with heavily edited images.
Requires good internet connection for first load.

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