mlsanityData to 7 Oct 2026

Defending Adversarial Examples by Negative Correlation Ensemble

arXiv 2206.10334 ↗First posted 11 Jun 2022cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return completely predictions by introducing carefully designed perturbations. Obviously, adversarial examples bring great security risks to the development of deep learning. Recently, Some defense approaches against adversarial examples have been proposed, however, in our opinion, the performance of these approaches are still limited. In this paper, we propose a new ensemble defense approach named the Negative Correlation Ensemble (NCEn), which achieves compelling results by introducing gradient directions and gradient magnitudes of each member in the ensemble negatively correlated and at the same time, reducing the transferability of adversarial examples among them. Extensive experiments have been conducted, and the results demonstrate that NCEn can improve the adversarial robustness of ensembles effectively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 11 Jun 2022.

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33 of 50 references.

References · 33

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21 more
  1. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
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