Visual Tripwires: Anticipating Failure in Deep Vision Systems
cs.CV, cs.LG
Submitted: 2026-09-23
Updated: 2026-09-23
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance.
Terminology
Abstract
Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance. Existing reliability methods typically evaluate uncertainty at individual time steps and do not explicitly model how a system progresses toward failure. We introduce Visual Tripwires, a predictive reliability framework that uses temporal instability in model behaviour to anticipate impending failure. Our central hypothesis is that predictive degradation develops progressively through measurable changes in latent representations, prediction trajectories, and attention structure. Visual Tripwires captures these changes using representation drift, prediction oscillation, trajectory curvature, and attention entropy. A lightweight tripwire predictor aggregates these signals over a temporal window to estimate the probability of failure within a future prediction horizon. Experiments across multiple datasets, architectures, and progressive perturbation settings show that the proposed instability signals emerge before predictive degradation and provide earlier and more accurate failure warnings than conventional uncertainty estimation methods. These results demonstrate that temporal instability contains useful information about future model reliability and provides a practical basis for early warning in deep vision systems.
Sources
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
- Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
- Tent: Fully Test-time Adaptation by Entropy Minimization
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