This report presents the winning solution for Task 2 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The goal of the challenge was to design and train a robust ANN-based model capable of achieving high accuracy in a binary classification task on both clean and adversarial data generated with the Random Distribution Shuffle Attack (RDSA). Our solution consists of two components: a data generation phase and a robust model training phase. In the first phase, we produced 15 million artificial training samples using a custom methodology derived from Random Distribution Shuffle Attack (RDSA). In the second phase, we introduced a robust architecture comprising (i)a Feature Embedding Block with shared weights among features of the same type and (ii)a Dense Fusion Tail responsible for the final prediction. Training this architecture on our adversarial dataset achieved a mixed accuracy score of 80\%, exceeding the second-place solution by two percentage points.

翻译：本报告介绍了ECML-PKDD 2025会议'对抗碰撞：高能物理发现中的鲁棒学习挑战赛'任务2的获胜解决方案。该挑战赛的目标是设计与训练一个基于人工神经网络的鲁棒模型，使其能够在由随机分布重排攻击生成的干净数据与对抗数据上，对二元分类任务均实现高精度分类。我们的解决方案包含两个组成部分：数据生成阶段与鲁棒模型训练阶段。在第一阶段，我们采用基于随机分布重排攻击的自定义方法生成了1500万个人工训练样本。在第二阶段，我们提出了一种鲁棒架构，该架构包含(i)具有同类型特征间权重共享的特征嵌入块，以及(ii)负责最终预测的密集融合尾部。使用我们的对抗数据集训练该架构，获得了80%的综合准确率分数，超出第二名解决方案两个百分点。