Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution

Dimitris Stefanopoulos; Andreas Voskou

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arXiv · 2510.16440

Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution

Dimitris Stefanopoulos·Andreas Voskou

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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.

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Dimitris Stefanopoulos, Andreas Voskou. 2025-10-18. Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution. https://arxiv.org/abs/2510.16440

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