Quantifying Fidelity: A Decisive Feature Approach to Comparing Synthetic and Real Imagery

TL;DR

This paper introduces Decisive Feature Fidelity (DFF), a behavior-grounded metric that assesses whether synthetic and real images lead to similar decision-driving features in autonomous systems, improving fidelity evaluation beyond visual similarity.

Contribution

The paper proposes DFF, a novel SUT-specific fidelity measure using explainable AI to compare decisive features in real and synthetic data, enhancing simulator calibration.

Findings

DFF uncovers discrepancies missed by traditional fidelity measures.

DFF-guided calibration improves decisive-feature fidelity.

Calibration maintains output value fidelity across models.

Abstract

Virtual testing using synthetic data has become a cornerstone of autonomous vehicle (AV) safety assurance. Despite progress in improving visual realism through advanced simulators and generative AI, recent studies reveal that pixel-level fidelity alone does not ensure reliable transfer from simulation to the real world. What truly matters is whether the system-under-test (SUT) bases its decisions on consistent decision evidence in both real and simulated environments, not just whether images "look real" to humans. To this end this paper proposes a behavior-grounded fidelity measure by introducing Decisive Feature Fidelity (DFF), a new SUT-specific metric that extends the existing fidelity spectrum to capture mechanism parity, that is, agreement in the model-specific decisive evidence that drives the SUT's decisions across domains. DFF leverages explainable-AI methods to identify and compare the decisive features driving the SUT's outputs for matched real-synthetic pairs. We further propose estimators based on counterfactual explanations, along with a DFF-guided calibration scheme to enhance simulator fidelity. Experiments on 2126 matched KITTI-VirtualKITTI2 pairs demonstrate that DFF reveals discrepancies overlooked by conventional output-value fidelity. Furthermore, results show that DFF-guided calibration improves decisive-feature and input-level fidelity without sacrificing output value fidelity across diverse SUTs.