Trusted-AI/adversarial-robustness-toolbox: papers linking to it

GitHub · 6,295 stars · 1,353 forks · Python · license MIT · created 2018 · github.com/Trusted-AI/adversarial-robustness-toolbox

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Trusted-AI/adversarial-robustness-toolbox (https://github.com/Trusted-AI/adversarial-robustness-toolbox), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the GitHub repository Trusted-AI/adversarial-robustness-toolbox in their Crossref records, the first in 2024; most of them (1) in 2026.
By category: AI (2) and Information Systems (1); the journals with the most are World Wide Web (1), Expert Systems with Applications (1) and Applied Intelligence (1).

By year#

20242026

Papers linking to Trusted-AI/adversarial-robustness-toolbox by publication year
Year	Papers
2024	1
2025	1
2026	1

Journals#

Journals whose papers link to Trusted-AI/adversarial-robustness-toolbox most
Journal	Papers
World Wide Web	1
Expert Systems with Applications	1
Applied Intelligence	1

Papers linking to Trusted-AI/adversarial-robustness-toolbox, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-02-24	Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untargeted white-box optimization adversarial attack	Expert Systems with Applications	Khushnaseeb Roshan, Aasim Zafar	46	cites it
2025-01-16	Fooling machine learning models: a novel out-of-distribution attack through generative adversarial networks	Applied Intelligence	Hailong Hu, Jun Pang	1	cites it
2026-10-10	A dual-layer security framework for AI systems: a lifecycle-aware security testing approach	World Wide Web	Hassan Chaitou, Njaka-Harilala Andriamandratomanana, Helmi Rais	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Trusted-AI/adversarial-robustness-toolbox: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-trusted-ai-adversarial-robustness-toolbox

@misc{scholar4ai-code-github-trusted-ai-adversarial-robustness-toolbox,
  author = {{Scholar4AI}},
  title = {Trusted-AI/adversarial-robustness-toolbox: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-trusted-ai-adversarial-robustness-toolbox}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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