Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms
arXiv:2606.04767v1 Announce Type: new Abstract: The robustness of deep neural networks is crucial for safety-critical deployments, yet existing evaluation methods are often attack-dependent and lack interpretability. We propose a principled, attack-agnostic robustness metric based on the spectral norm of the Fisher Information Matrix (FIM), which quantifies the worst-case sensitivity of the model's output distribution to input perturbations. Theoretically, we establish that the FIM equals the variance of the input Jacobian and derive closed-form spectral bounds for common architectures, includ
The increasing deployment of AI in safety-critical applications necessitates robust and interpretable measures of model reliability, pushing research towards more principled evaluation methods.
A principled, attack-agnostic metric for AI robustness will accelerate the adoption of AI in sensitive domains by increasing trust and enabling more reliable system design.
The ability to measure model robustness independent of specific attack vectors allows for more generalizable and interpretable safety assurances for AI systems.
- · AI developers
- · Safety-critical industries
- · Certification bodies
- · Research institutions
- · AI systems without demonstrable robustness
- · Attack-dependent robustness evaluation methods
This metric standardizes and improves the evaluation of AI model security and reliability.
It will likely lead to new methodologies for designing intrinsically more robust AI models.
Increased trust in AI systems could accelerate widespread adoption in highly regulated sectors, driving further innovation and economic integration.
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Read at arXiv cs.LG