Adversarial Robustness Assessment of NeuroEvolution Approaches
- URL: http://arxiv.org/abs/2207.05451v1
- Date: Tue, 12 Jul 2022 10:40:19 GMT
- Title: Adversarial Robustness Assessment of NeuroEvolution Approaches
- Authors: In\^es Valentim, Nuno Louren\c{c}o, Nuno Antunes
- Abstract summary: We evaluate the robustness of models found by two NeuroEvolution approaches on the CIFAR-10 image classification task.
Our results show that when the evolved models are attacked with iterative methods, their accuracy usually drops to, or close to, zero.
Some of these techniques can exacerbate the perturbations added to the original inputs, potentially harming robustness.
- Score: 1.237556184089774
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: NeuroEvolution automates the generation of Artificial Neural Networks through
the application of techniques from Evolutionary Computation. The main goal of
these approaches is to build models that maximize predictive performance,
sometimes with an additional objective of minimizing computational complexity.
Although the evolved models achieve competitive results performance-wise, their
robustness to adversarial examples, which becomes a concern in
security-critical scenarios, has received limited attention. In this paper, we
evaluate the adversarial robustness of models found by two prominent
NeuroEvolution approaches on the CIFAR-10 image classification task: DENSER and
NSGA-Net. Since the models are publicly available, we consider white-box
untargeted attacks, where the perturbations are bounded by either the L2 or the
Linfinity-norm. Similarly to manually-designed networks, our results show that
when the evolved models are attacked with iterative methods, their accuracy
usually drops to, or close to, zero under both distance metrics. The DENSER
model is an exception to this trend, showing some resistance under the L2
threat model, where its accuracy only drops from 93.70% to 18.10% even with
iterative attacks. Additionally, we analyzed the impact of pre-processing
applied to the data before the first layer of the network. Our observations
suggest that some of these techniques can exacerbate the perturbations added to
the original inputs, potentially harming robustness. Thus, this choice should
not be neglected when automatically designing networks for applications where
adversarial attacks are prone to occur.
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