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Recently, outlier exposure (OE)-based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin-bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art methods on various benchmarking metrics. The code is publicly available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/lakpa-tamang9\/margin_ood\/tree\/kais\" ext-link-type=\"uri\">https:\/\/github.com\/lakpa-tamang9\/margin_ood\/tree\/kais<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1007\/s10115-025-02380-y","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:59:25Z","timestamp":1743094765000},"page":"5541-5569","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving out-of-distribution detection by enforcing confidence margin"],"prefix":"10.1007","volume":"67","author":[{"given":"Lakpa","family":"Tamang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Reda","family":"Bouadjenek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Dazeley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Aryal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"key":"2380_CR1","unstructured":"Yang J, Zhou K, Li Y, Liu Z (2021) Generalized out-of-distribution detection: a survey. arXiv preprint arXiv:2110.11334"},{"key":"2380_CR2","unstructured":"Xu P, Ehinger KA, Zhang Y, Finkelstein A, Kulkarni SR, Xiao J (2015) TurkerGaze: crowdsourcing saliency with webcam based eye tracking"},{"key":"2380_CR3","first-page":"32598","volume":"35","author":"J Yang","year":"2022","unstructured":"Yang J, Wang P, Zou D, Zhou Z, Ding K, Peng W, Wang H, Chen G, Li B, Sun Y et al (2022) Openood: Benchmarking generalized out-of-distribution detection. 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