针对高能物理分类模型，设计高效小扰动攻击方法并夺冠。
Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution
- 多轮梯度优化结合随机初始化与样本混合提升攻击效果。
- 在最小扰动下实现最高误分类率，击败其他参赛方案。
- 适合研究对抗攻击、模型鲁棒性或高能物理安全性的学者。
本文介绍在ECML-PKDD 2025高能物理发现中的鲁棒学习挑战赛任务1的冠军解决方案。该任务要求设计一种对抗攻击，以最大化分类错误的同时最小化输入扰动。我们的方法采用多轮基于梯度的策略，利用模型的可微结构，并引入随机初始化和样本混合技术以增强攻击有效性。所提出的攻击在扰动幅度和欺骗成功率两方面均表现最优，最终获得竞赛第一名。
原文摘要 · Abstract (English)
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.
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