A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification
Journal:
arXiv
Published Date:
Dec 28, 2024
Abstract
Deep learning-based discriminative classifiers, despite their remarkable
success, remain vulnerable to adversarial examples that can mislead model
predictions. While adversarial training can enhance robustness, it fails to
address the intrinsic vulnerability stemming from the opaque nature of these
black-box models. We present a deep ensemble model that combines discriminative
features with generative models to achieve both high accuracy and adversarial
robustness. Our approach integrates a bottom-level pre-trained discriminative
network for feature extraction with a top-level generative classification
network that models adversarial input distributions through a deep latent
variable model. Using variational Bayes, our model achieves superior robustness
against white-box adversarial attacks without adversarial training. Extensive
experiments on CIFAR-10 and CIFAR-100 demonstrate our model's superior
adversarial robustness. Through evaluations using counterfactual metrics and
feature interaction-based metrics, we establish correlations between model
interpretability and adversarial robustness. Additionally, preliminary results
on Tiny-ImageNet validate our approach's scalability to more complex datasets,
offering a practical solution for developing robust image classification
models.
