AdvSecureNet: A Python Toolkit for Adversarial Machine Learning

• URL: http://arxiv.org/abs/2409.02629v1
• Date: Wed, 4 Sep 2024 11:47:00 GMT
• Title: AdvSecureNet: A Python Toolkit for Adversarial Machine Learning
• Authors: Melih Catal, Manuel Günther,
• Abstract summary: AdvSecureNet is a PyTorch based toolkit for adversarial machine learning. It is the first toolkit that supports both CLI and API interfaces and external YAML configuration files. The project is available as an open-source project on GitHub.
• Score: 1.3812010983144798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models are vulnerable to adversarial attacks. Several tools have been developed to research these vulnerabilities, but they often lack comprehensive features and flexibility. We introduce AdvSecureNet, a PyTorch based toolkit for adversarial machine learning that is the first to natively support multi-GPU setups for attacks, defenses, and evaluation. It is the first toolkit that supports both CLI and API interfaces and external YAML configuration files to enhance versatility and reproducibility. The toolkit includes multiple attacks, defenses and evaluation metrics. Rigiorous software engineering practices are followed to ensure high code quality and maintainability. The project is available as an open-source project on GitHub at https://github.com/melihcatal/advsecurenet and installable via PyPI.

Related papers

• A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning [42.350737545269105]
  We show how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.
  arXiv  Detail & Related papers  (2024-07-19T23:01:48Z)
• VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models [89.63342806812413]
  We present an open-source toolkit for evaluating large multi-modality models based on PyTorch. VLMEvalKit implements over 70 different large multi-modality models, including both proprietary APIs and open-source models. We host OpenVLM Leaderboard to track the progress of multi-modality learning research.
  arXiv  Detail & Related papers  (2024-07-16T13:06:15Z)
• Chain of Tools: Large Language Model is an Automatic Multi-tool Learner [54.992464510992605]
  Automatic Tool Chain (ATC) is a framework that enables the large language models (LLMs) to act as a multi-tool user. To scale up the scope of the tools, we next propose a black-box probing method. For a comprehensive evaluation, we build a challenging benchmark named ToolFlow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• Python Fuzzing for Trustworthy Machine Learning Frameworks [0.0]
  We propose a dynamic analysis pipeline for Python projects using Sydr-Fuzz. Our pipeline includes fuzzing, corpus minimization, crash triaging, and coverage collection. To identify the most vulnerable parts of machine learning frameworks, we analyze their potential attack surfaces and develop fuzz targets for PyTorch, and related projects such as h5py.
  arXiv  Detail & Related papers  (2024-03-19T13:41:11Z)
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [104.37772295581088]
  Open-source large language models (LLMs), e.g., LLaMA, remain significantly limited in tool-use capabilities. We introduce ToolLLM, a general tool-usetuning encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning framework for tool use, which is constructed automatically using ChatGPT.
  arXiv  Detail & Related papers  (2023-07-31T15:56:53Z)
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series [0.0]
  PyPOTS is an open-source Python library dedicated to data mining and analysis on partially-observed time series. It provides easy access to diverse algorithms categorized into four tasks: imputation, classification, clustering, and forecasting.
  arXiv  Detail & Related papers  (2023-05-30T07:57:05Z)
• Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility [5.669983975369642]
  Python applications may lose important data, such as trained models and extracted features, due to machine failures or human errors. This paper presents our vision of transactional Python that provides DART without any code modifications to user programs or the Python kernel. Our evaluation of a proof-of-concept implementation with public PyTorch and scikit-learn applications shows that DART can be offered with overheads ranging 1.5%--15.6%.
  arXiv  Detail & Related papers  (2023-05-15T16:27:09Z)
• BackdoorBox: A Python Toolbox for Backdoor Learning [67.53987387581222]
  This Python toolbox implements representative and advanced backdoor attacks and defenses. It allows researchers and developers to easily implement and compare different methods on benchmark or their local datasets.
  arXiv  Detail & Related papers  (2023-02-01T09:45:42Z)
• PyRelationAL: a python library for active learning research and development [1.0061110876649197]
  Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data. Here, we introduce PyRelationAL, an open source library for AL research. We describe a modular toolkit based around a two step design methodology for composing pool-based active learning strategies.
  arXiv  Detail & Related papers  (2022-05-23T08:21:21Z)
• PyTorchVideo: A Deep Learning Library for Video Understanding [71.89124881732015]
  PyTorchVideo is an open-source deep-learning library for video understanding tasks. It covers a full stack of video understanding tools including multimodal data loading, transformations, and models. The library is based on PyTorch and can be used by any training framework.
  arXiv  Detail & Related papers  (2021-11-18T18:59:58Z)
• SafePILCO: a software tool for safe and data-efficient policy synthesis [67.17251247987187]
  SafePILCO is a software tool for safe and data-efficient policy search with reinforcement learning. It extends the known PILCO algorithm, originally written in Python, to support safe learning.
  arXiv  Detail & Related papers  (2020-08-07T17:17:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.