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Magika by Google

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Magika is a deep learning-based tool for detecting and classifying various file content types. Developed by Google, it's designed to outperform traditional file type detection tools by providing enhanced accuracy across a broad range of content types.

Magika is designed for efficiency, allowing for quick operation even on a single CPU. Users can test out Magika's capabilities from their browser. Uploaded files remains secure as the processing is entirely performed browser-side with no uploads to external servers.

A unique feature of Magika is its installability as a Python package, allowing users to run it readily from their command line. It can also be leveraged in Python or JavaScript codebases, making it a versatile tool in a developer's kit.

Magika is a game-changer that allows precise file content type detection with comprehensive support including language-specific files, executables, document types, image and video data, and audio bitstream data, among others.

Reports indicate that a similar version of Magika is in use at Google, scanning millions of files per second for accurate content-type tagging. Plans are underway to release a detailed paper explaining how Magika was trained and its performance on large datasets.Despite its capabilities, users should note that Magika is designed to output a single content type for a file, therefore polyglot files will not be mapped to two or more categories.

Despite this, it remains a powerful tool in content type detection using deep learning. For users wanting to cite Magika, a citation guide is available on the project's GitHub page.

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Pros & Cons


  • Outperforms traditional tools
    Enhanced accuracy
    Efficient operation
    Operates on single CPU
    Browser-side file processing
    High file security
    Installs as Python package
    Command-line operation
    Python or JavaScript integration
    Comprehensive file type support
    Scans millions files/second
    Language-specific file support
    Executable, document, image, video support
    Audio bitstream data support
    99%+ average precision
    99%+ average recall
    Demo option in browser
    Detailed performance paper
    Citable with citation guide
    Faster file-type identification
    Commands to install
    Example outputs provided
    JavaScript library usage
    Single content output
    Model details disclosed
    Model owners clarified
    Detailed performance metrics
    Limitations specified
    Use cases identified
    Outputs file total size
    Content type probability displayed
    Outputs individual file precision
    Outputs individual file recall
    Detailed quantitative analysis
    Can process large datasets
    Designed for developer usage
    Deep learning-based precision
    Output compatible with data tagging
    Can process polyglot files
    Comprehensive support for executable types
    Scaled successfully at Google
    Optimized for Python and JavaScript
    Processed in client-side browser
    Consistently updated and maintained
    Fast even on single CPU
    Handles document files effectively
    Support for audio and video data
    Recognizes language-specific files


  • Single content-type output limitation
    Browser-side-only processing
    No support for external servers
    Lack of detailed training documentation
    Python and JavaScript only

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