Certified Robustness via Sparse Second-Order Cone Relaxations
The paper introduces a hierarchy of sparse Second-Order Cone Programming (SOCP) relaxations to certify neural network robustness against adversarial attacks. This method balances accuracy and computational cost by combining ReLU activation convex-hull relaxations with selective second-order constraints, offering stronger guarantees than linear programming without the high cost of global semidefinite programming.