Deep vision systems warn of failure using temporal instability cues
Visual Tripwires: Anticipating Failure in Deep Vision Systems
Computer Vision and Pattern RecognitionMachine Learning
Summary
Deep vision systems can sometimes fail due to changes like corruption or occlusion, and current methods only check for uncertainty at each moment without looking ahead. The authors show that tracking how the system's internal signals change over time can predict failures before they happen. They measure changes such as shifts in how the model represents images, unstable predictions, and changes in attention focus. Their approach gives earlier and more accurate warnings of impending failure than traditional methods, making it easier to catch problems in advance.
What this means in practice
- •For autonomous vehicle developers: Provide early failure warnings in vehicle perception systems to improve safety under challenging environmental conditions.
- •For security camera operators: Detect and anticipate failures in surveillance vision systems due to occlusion or poor lighting before they impact monitoring.
Authors
Anoushka Harit, Rehan Zuberi, William Prew, Florian Markowetz
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.