The Shape of Noise: Layer-Wise Perturbation Profiles for Diagnosing Vision Robustness

Son Nguyen, V. G. Bao, Quang Minh Phan, Trong P. Le

Keywords: Robust CNN, explainable benchmark

Abstract: As vision models are increasingly applied across diverse applications, the need for explainable and robust architectures continues to grow. Existing corruption robustness benchmarks, such as CIFAR-10-C, reduce model behavior to aggregate metrics like mean Corruption Error, obscuring how perturbations are amplified, suppressed, or transformed within a network. We introduce the Perturbation Evaluation Framework (PEF), a layer-wise diagnostic framework that decomposes a model’s response to input corruption and reveals architecture-dependent amplification and suppression signatures. Through experiments including intermediate-layer perturbation injection and profile-guided stabilization, we demonstrate that PEF complements aggregate benchmarks by providing interpretable layer-wise diagnostics for analyzing and targeting robustness behavior.

Paper Type: Short (4 pages)

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Submission Number: 118