@inproceedings{67300,
  abstract     = {{Deep neural networks (DNNs) are vulnerable to small adversarial perturbations, which are tiny changes to the input data that appear insignificant but cause the model to produce drastically different outputs. Many defense methods require modifying model architectures during evaluation or performing test-time data purification. This not only introduces additional complexity but is often architecture-dependent. We show, however, that robust feature learning during training can significantly enhance DNN robustness. We propose MOREL, a multi-objective approach that aligns natural and adversarial features using cosine similarity and multi-positive contrastive losses to encourage similar features for same-class inputs. Extensive experiments demonstrate that MOREL significantly improves robustness against both white-box and black-box attacks. Our code is available at https://github.com/salomonhotegni/MOREL.}},
  author       = {{Hotegni, Sedjro Salomon and Peitz, Sebastian}},
  booktitle    = {{Artificial Neural Networks and Machine Learning – ICANN 2025}},
  editor       = {{Senn, Walter and Sanguineti, Marcello and Saudargiene, Ausra and Tetko, Igor V. and Villa, Alessandro E. P. and Jirsa, Viktor and Bengio, Yoshua}},
  isbn         = {{978-3-032-04558-4}},
  keywords     = {{own, own-conference}},
  pages        = {{442–454}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Enhancing Adversarial Robustness Through Multi-objective Representation Learning}}},
  doi          = {{10.1007/978-3-032-04558-4_35}},
  year         = {{2026}},
}