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

Object detectors used in safety-critical systems are vulnerable to physical adversarial patch attacks (images or objects designed to fool AI vision systems), which work by disrupting how information flows through layers of the neural network rather than simply corrupting visual features. The paper proposes CaDe, a defense method that uses hierarchical stability constraints and adaptive sparse causal decoupling to stop adversarial perturbations from propagating through the network layers, improving detection accuracy by 5-6% against these attacks while maintaining real-time performance.