{"ID":2856122,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11018","arxiv_id":"2510.11018","title":"The Easy Path to Robustness: Coreset Selection using Sample Hardness","abstract":"Designing adversarially robust models from a data-centric perspective requires understanding which input samples are most crucial for learning resilient features. While coreset selection provides a mechanism for efficient training on data subsets, current algorithms are designed for clean accuracy and fall short in preserving robustness. To address this, we propose a framework linking a sample's adversarial vulnerability to its \\textit{hardness}, which we quantify using the average input gradient norm (AIGN) over training. We demonstrate that \\textit{easy} samples (with low AIGN) are less vulnerable and occupy regions further from the decision boundary. Leveraging this insight, we present EasyCore, a coreset selection algorithm that retains only the samples with low AIGN for training. We empirically show that models trained on EasyCore-selected data achieve significantly higher adversarial accuracy than those trained with competing coreset methods under both standard and adversarial training. As AIGN is a model-agnostic dataset property, EasyCore is an efficient and widely applicable data-centric method for improving adversarial robustness. We show that EasyCore achieves up to 7\\% and 5\\% improvement in adversarial accuracy under standard training and TRADES adversarial training, respectively, compared to existing coreset methods.","short_abstract":"Designing adversarially robust models from a data-centric perspective requires understanding which input samples are most crucial for learning resilient features. While coreset selection provides a mechanism for efficient training on data subsets, current algorithms are designed for clean accuracy and fall short in pre...","url_abs":"https://arxiv.org/abs/2510.11018","url_pdf":"https://arxiv.org/pdf/2510.11018v1","authors":"[\"Pranav Ramesh\",\"Arjun Roy\",\"Deepak Ravikumar\",\"Kaushik Roy\",\"Gopalakrishnan Srinivasan\"]","published":"2025-10-13T05:28:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CV\"]","methods":"[]","has_code":false}