[
    {
        "id": "osp-22076",
        "type": "article-journal",
        "title": "eval-unlearn: Benchmarking unlearning in Text-to-Image Diffusion Models",
        "author": [
            {
                "family": "Mansi",
                "given": ""
            },
            {
                "family": "Raghavan",
                "given": "Nikhil"
            },
            {
                "family": "Huang",
                "given": "Zixia"
            },
            {
                "family": "Ong",
                "given": "Kai Sheng"
            },
            {
                "family": "Lim",
                "given": "Ji Shen"
            },
            {
                "family": "Ling",
                "given": "Brandon Siao Xiang"
            },
            {
                "family": "Leofante",
                "given": "Francesco"
            }
        ],
        "URL": "https://omanscience.com/en/articles/eval-unlearn-benchmarking-unlearning-in-text-to-image-diffusion-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "The rising number of concept unlearning techniques for text-to-image (T2I) diffusion models has produced a fragmented evaluation landscape. Methods are assessed under heterogeneous experimental conditions making principled cross-method comparison difficult. We present eval-unlearn, an open-source Python library providing a unified, reproducible benchmarking framework for concept unlearning in T2I Diffusion models. eval-unlearn integrates twelve published unlearning techniques spanning fine-tuning, closed-form model editing, and inference-time intervention, alongside nine complementary evaluation metrics covering erasure efficacy, adversarial robustness, generative quality, and concept retention. Its plugin architecture lets third-party techniques and metrics self-register without modifying the core framework, and its streaming, batched pipeline supports efficient evaluation of both standard NSFW concepts and arbitrary general concepts. As a further contribution, we release a public leaderboard on HuggingFace along with an interactive tool for real-time evaluation of unlearning techniques. The leaderboard compares nudity concept erasure case study across all twelve techniques, exposing significant accuracy-quality trade-offs that are obscured by heterogeneous evaluation. eval-unlearn is released under the MIT license; the package, code, leaderboard, and documentation are all available at https://eval-unlearn.readthedocs.io."
    }
]