The task of content-type detection—which entails identifying the data encoded in an arbitrary byte sequence—is critical for operating systems, development, reverse engineering environments, and a variety of security applications. In this paper, we introduce Magika, a novel AI-powered content-type detection tool. Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model's weights. We show that Magika achieves an average F1 score of 99% across over a hundred content types and a test set of more than 1M files, outperforming all existing content-type detection tools today. To foster adoption and improvements, we open source Magika under an Apache 2 license on GitHub and we make our model and training pipeline publicly available. Our tool has already seen adoption by Gmail and Google Drive for attachment scanning, by VirusTotal to aid with malware analysis, and by prominent open-source projects such as Apache Tika. While this paper focuses on the initial version, Magika continues to evolve with support for over 200 content types now available. The latest developments can be found at https://github.com/google/magika.

This paper introduces RETSim (Resilient and Efficient Text Similarity), a lightweight, multilingual deep learning model trained to produce robust metric embeddings for near-duplicate text retrieval, clustering, and dataset deduplication tasks. We demonstrate that RETSim is significantly more robust and accurate than MinHash and neural text embeddings, achieving new state-of-the-art performance on dataset deduplication, adversarial text retrieval benchmarks, and spam clustering tasks. Additionally, we introduce the W4NT3D benchmark (Wiki-40B 4dversarial Near-T3xt Dataset), enabling the evaluation of models on typo-laden near-duplicate text retrieval in a multilingual setting. RETSim and the W4NT3D benchmark are released under the MIT License at https://github.com/google/unisim.

This paper describes RETVec, an efficient, resilient, and multilingual text vectorizer designed for neural-based text processing. RETVec combines a novel character encoding with an optional small embedding model to embed words into a 256-dimensional vector space. The RETVec embedding model is pre-trained using pair-wise metric learning to be robust against typos and character-level adversarial attacks. In this paper, we evaluate and compare RETVec to state-of-the-art vectorizers and word embeddings on popular model architectures and datasets. These comparisons demonstrate that RETVec leads to competitive, multilingual models that are significantly more resilient to typos and adversarial text attacks. RETVec is available under the Apache 2 license at https://github.com/google-research/retvec.