{"ID":2853388,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16440","arxiv_id":"2510.16440","title":"Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution","abstract":"This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The task required designing an adversarial attack against a provided classification model that maximizes misclassification while minimizing perturbations. Our approach employs a multi-round gradient-based strategy that leverages the differentiable structure of the model, augmented with random initialization and sample-mixing techniques to enhance effectiveness. The resulting attack achieved the best results in perturbation size and fooling success rate, securing first place in the competition.","short_abstract":"This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The task required designing an adversarial attack against a provided classification model that maximizes misclassification while minimizing perturbations...","url_abs":"https://arxiv.org/abs/2510.16440","url_pdf":"https://arxiv.org/pdf/2510.16440v1","authors":"[\"Dimitris Stefanopoulos\",\"Andreas Voskou\"]","published":"2025-10-18T10:26:47Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CR\"]","methods":"[]","has_code":false}