Propose a design change to improve the accuracy and efficiency of gradient computation for complex neural network architectures. Explain how you would address the tradeoff between using finite differencing and random projections.

Implement a hybrid approach that uses both centered difference approximation with large enough perturbation sizes and random projections at both input and output to ensure accurate gradient computation while minimizing computational overhead.