Building Safe & Resource Efficient NN through architectural collapse

Modern Convolutional Neural Networks (CNNs) often suffer from depth redundancy, where sequential layers increase inference latency and memory usage without significantly improving accuracy. To address this, we introduce Layer-by-Layer Structural Reduction (LBL), a post-hoc methodology that identifies contiguous layer blocks and collapses them into a single, lightweight surrogate module. By targeting depth redundancy directly, LBL aims to reduce sequential operations and increase hardware efficiency. While LBL streamlines networks for deployment, ensuring these resource-efficient models remain secure is critical. We are working on evaluating model resilience using Adversarial Particle Swarm Optimization (APSO), a data-agnostic, black-box framework that generates transferable adversarial examples to probe cross-model vulnerabilities. This ongoing work investigates how combining structural collapse with reproducible robustness metrics and Explainable AI (XAI) diagnostics can establish a unified paradigm for building and validating deep learning systems that are both computationally efficient and resilient to inference-time poisoning.