---
title: "Trusted-AI/adversarial-robustness-toolbox"
description: "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."
url: https://scholar4ai.com/code/github-trusted-ai-adversarial-robustness-toolbox
markdown_url: https://scholar4ai.com/code/github-trusted-ai-adversarial-robustness-toolbox.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

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

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.

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

All repositories: https://scholar4ai.com/code.md

## 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

*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](https://scholar4ai.com/journals/world-wide-web.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |

## Papers

*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](https://doi.org/10.1016/j.eswa.2024.123567) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Khushnaseeb Roshan, Aasim Zafar | 46 | cites it |
| 2025-01-16 | [Fooling machine learning models: a novel out-of-distribution attack through generative adversarial networks](https://doi.org/10.1007/s10489-024-05974-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Hailong Hu, Jun Pang | 1 | cites it |
| 2026-10-10 | [A dual-layer security framework for AI systems: a lifecycle-aware security testing approach](https://doi.org/10.1007/s11280-026-01437-4) | [World Wide Web](https://scholar4ai.com/journals/world-wide-web.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).