# Fast And Accurate Text Content File Type Identification

arXiv: https://arxiv.org/abs/2609.21306 · HTML: https://mlsanity.com/paper/2609.21306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 18 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [A Survey of Machine Learning Methods and Challenges for Windows Malware Classification](https://mlsanity.com/paper/2006.09271/) (2020)
- [Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection](https://mlsanity.com/paper/2012.09390/) (2020)
- [Lempel-Ziv Jaccard Distance, an Effective Alternative to Ssdeep and Sdhash](https://mlsanity.com/paper/1708.03346/) (2017)
- [Magika: AI-Powered Content-Type Detection](https://mlsanity.com/paper/2409.13768/) (2024)
- [Automatic Classification of Object Code Using Machine Learning](https://mlsanity.com/paper/1805.02146/) (2018)

## Abstract (arXiv)

A common requirement across organizations is to have a tool that can identify file types based on their contents, particularly in the cybersecurity domain where magic numbers and file extensions can not be trusted. While existing tools work well in practice, there is plenty of room for improvement either in terms of computational load and time for detection in the case of model based tools like Magika or in terms of accuracy of detection in the case of file parsing tools that use programming language constructs. In this study, we propose a neural network model for identification of types of text content files, especially source code, that is more accurate and faster than other available tools. Our experiments on open-source files indicate that it is not only more accurate on average for text-content file-type identification, but also approximately four times faster than Magika, while being 28% smaller in size.