CaDe: Adaptive Sparse Causal Decoupling for Adversarially Robust Object Detection via Hierarchical Stability Constraints

Summary

This paper proposes CaDe, a defense for object detectors against physical adversarial patch attacks. It argues that patches mainly disrupt operational causal consistency across the layers of a deep neural network rather than corrupting feature values directly. CaDe combines hierarchical stability constraints with adaptive sparse causal decoupling, and reports mAP gains of 6.19% against Hiding Attack and 5.53% against Appearing Attack over the best baseline, with a 0.14% mAP drop on benign samples.

Mitigation

CaDe mitigates the hierarchical propagation of residual perturbations through hierarchical stability constraints and adaptive sparse causal decoupling strategies.

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