In the digital sphere, identifying the type of files we find is crucial to guarantee security. However, with the increasing complexity and diversity of file formats, accurately detecting file content becomes a challenge. Existing solutions often face limitations in precision and recall, leaving room to improve file type detection.
Magic comes in as a novel ai-powered solution to address the need for a more accurate and efficient file type detection tool. Magika addresses the common problem of misidentifying file types using deep learning technology. Unlike existing tools that can struggle with accuracy, Magika is based on a custom, highly optimized Keras model that weighs only around 1 MB. This allows for fast and accurate file identification, even when running on a single CPU.
Magika's performance is really noteworthy, especially when compared to existing approaches. In an evaluation involving more than 1 million files and covering more than 100 content types, including binary and textual formats, Magika achieves a remarkable 99% or higher in both precision and recall. This means it correctly identifies files and minimizes false positives or negatives.
The tool offers multiple modes of accessibility, available as a Python command line, a Python API, and even an experimental version of TFJS. Magika, trained on a substantial data set of over 25 million files across various content types, exhibits near-constant inference time, taking only about five milliseconds per file after loading the model. Its ability to process batches of files simultaneously further improves its efficiency.
A unique feature of Magika lies in its threshold system per content type. This system helps determine the level of confidence in the model's prediction for each file type, allowing for more precise and nuanced results. Additionally, Magika supports three prediction modes (high confidence, medium confidence, and best estimate) that accommodate different levels of error tolerance.
In conclusion, Magika emerges as a powerful and efficient solution to the challenge of file type detection. Its impressive metrics and versatile accessibility make it a valuable tool for improving security, especially in large-scale applications like Gmail, Drive, and Safe Browsing. With an open invitation to community collaboration, Magika represents a positive step toward improving the accuracy and reliability of file type detection in the digital landscape.
Facility
Magika is available as magika is PyPI:
$ pip install magika
Niharika is a Technical Consulting Intern at Marktechpost. She is a third-year student currently pursuing her B.tech degree at the Indian Institute of technology (IIT), Kharagpur. She is a very enthusiastic person with a keen interest in machine learning, data science and artificial intelligence and an avid reader of the latest developments in these fields.
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