🤖 AI Summary
This work addresses the limitation of existing output-confidence–based fault detection methods, which often fail to capture internal errors in neural networks. The authors propose Self-Detecting Neural Networks (SDNN), a novel framework that introduces the concept of “spectral drift” to reveal that erroneous predictions manifest as pronounced multi-scale spectral instabilities in internal activations. Spectral features are extracted via short-time Fourier transform, wavelet decomposition, and statistical moments, and a lightweight detector is trained using curriculum learning to establish an end-to-end learnable internal monitoring mechanism. Evaluated on CIFAR-10, SDNN achieves an AUROC of 79.0 ± 25.3%, outperforming baseline methods such as MaxSoftmax and Energy Score by 25–30 percentage points.
📝 Abstract
Neural network misclassifications exhibit characteristic spectral instability in internal activations that is invisible at the output layer. This phenomenon is identified and formalized as Spectral Drift -- the frequency-domain distance between consecutive layer activations -- with empirical validation showing that failures exhibit significantly higher drift than correct predictions (1.9% increase, p<0.001). This spectral signature emerges during internal processing but becomes masked in final outputs, explaining why confidence-based detection methods struggle.
This work introduces Self-Detecting Neural Networks (SDNN), a framework that monitors spectral dynamics across network depth using Short-Time Fourier Transform, wavelet decomposition, and statistical moments to capture multi-scale spectral features. A lightweight detector network (5% parameter overhead) learns to identify failure-indicative patterns via curriculum learning on progressively challenging distributions: natural misclassifications, distribution shifts, and adversarial perturbations.
Experiments on CIFAR-10 demonstrate that SDNN achieves 79.0 +/- 25.3% AUROC across three seeds, substantially outperforming confidence-based baselines including MaxSoftmax (50.5%) and Energy Score (52.9%) by approximately 25-30 percentage points. Ablation studies reveal that wavelet decomposition and statistical features make consistent contributions, while STFT's role remains unclear. This work establishes spectral analysis of internal activations as a promising direction for neural network reliability, revealing diagnostic information inaccessible to output-based approaches.