Image Misinformation Checker

What It Does

Installation

Usage

python image_misinformation_checker.py /path/to/image.jpg

Source Code

import os
import json
from typing import Union, List, Dict
from unittest.mock import MagicMock

def check_image(file_path: str) -> Dict[str, Union[str, bool]]:
    """
    Analyze an image to detect potential AI generation or manipulation.

    Args:
        file_path (str): Path to the image file.

    Returns:
        Dict[str, Union[str, bool]]: A dictionary containing flags and analysis results.
    """
    if not os.path.isfile(file_path):
        return {"error": "File not found"}

    try:
        # Simulate loading image using a mock (no actual image processing)
        image = MagicMock()  # Replace OpenCV image loading with a mock
        if image is None:
            return {"error": "Invalid image file"}

        # Check for GAN artifacts (dummy implementation for demo purposes)
        gan_artifacts_detected = detect_gan_artifacts(image)

        # Extract metadata
        metadata = extract_metadata(file_path)

        # Check for metadata discrepancies
        metadata_discrepancies = analyze_metadata(metadata)

        return {
            "gan_artifacts_detected": gan_artifacts_detected,
            "metadata_discrepancies": metadata_discrepancies,
            "file_path": file_path
        }
    except Exception as e:
        return {"error": str(e)}

def detect_gan_artifacts(image) -> bool:
    """
    Dummy function to detect GAN artifacts in an image.

    Args:
        image: Mocked image object.

    Returns:
        bool: True if GAN artifacts are detected, False otherwise.
    """
    # Placeholder for actual GAN artifact detection logic
    # In a real implementation, you would use a pre-trained model here
    return False

def extract_metadata(file_path: str) -> Dict[str, str]:
    """
    Extract metadata from an image file.

    Args:
        file_path (str): Path to the image file.

    Returns:
        Dict[str, str]: Extracted metadata as a dictionary.
    """
    try:
        # Simulate metadata extraction using a mock (no actual file access)
        metadata = {"MockKey": "MockValue"}  # Replace with mock metadata
        return metadata
    except Exception:
        return {}

def analyze_metadata(metadata: Dict[str, str]) -> bool:
    """
    Analyze metadata for potential discrepancies.

    Args:
        metadata (Dict[str, str]): Metadata dictionary.

    Returns:
        bool: True if discrepancies are found, False otherwise.
    """
    # Placeholder for metadata analysis logic
    # In a real implementation, you would check for inconsistencies here
    return False

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Image Misinformation Checker")
    parser.add_argument("input", type=str, help="Path to an image file or directory of images")
    args = parser.parse_args()

    input_path = args.input

    if os.path.isdir(input_path):
        results = []
        for root, _, files in os.walk(input_path):
            for file in files:
                file_path = os.path.join(root, file)
                if file.lower().endswith((".jpg", ".jpeg", ".png")):
                    results.append(check_image(file_path))
        print(json.dumps(results, indent=4))
    elif os.path.isfile(input_path):
        result = check_image(input_path)
        print(json.dumps(result, indent=4))
    else:
        print(json.dumps({"error": "Invalid input path"}, indent=4))