  The vulnerability of Deep Neural Networks to adversarial perturbations
presents significant security concerns, as the imperceptible perturbations can
contaminate the feature space and lead to incorrect predictions. Recent studies
have attempted to calibrate contaminated features by either suppressing or
over-activating particular channels. Despite these efforts, we claim that
adversarial attacks exhibit varying disruption levels across individual
channels. Furthermore, we argue that harmonizing feature maps via graph and
employing graph convolution can calibrate contaminated features. To this end,
we introduce an innovative plug-and-play module called Feature Map-based
Reconstructed Graph Convolution (FMR-GC). FMR-GC harmonizes feature maps in the
channel dimension to reconstruct the graph, then employs graph convolution to
capture neighborhood information, effectively calibrating contaminated
features. Extensive experiments have demonstrated the superior performance and
scalability of FMR-GC. Moreover, our model can be combined with advanced
adversarial training methods to considerably enhance robustness without
compromising the model's clean accuracy.
