arXiv:2606.02876v1 Announce Type: new Abstract: Randomized smoothing (RS) uses a smoothed classifier to provide architecture-agnostic certificates of $\ell_2$ classification robustness, but its dependence on per-input Monte Carlo (MC) sampling undermines its use in real-time systems. We argue that this cost is structural rather than fundamental, such that it can be significantly reduced by sharing information across the deployment stream. We introduce RRISE, an RS framework that compresses certification into a single forward pass through a learned surrogate. RRISE trains the surrogate against
The increasing demand for real-time AI systems necessitates more efficient robustness certification methods, pushing research towards solutions that overcome the computational costs of traditional Monte Carlo sampling.
This development could significantly accelerate the deployment of robust AI models in critical real-time applications by making their certification more computationally feasible and less resource-intensive.
The method of certifying the robustness of AI models shifts from expensive, per-input Monte Carlo sampling to a more efficient single forward pass via a learned surrogate, enabling broader adoption in operational systems.
- · AI developers
- · Real-time AI system providers
- · Sectors reliant on robust AI (e.g., autonomous vehicles, defense)
- · Providers of computationally intensive AI robustness tools
Reduced computational overhead for deploying provably robust AI systems.
Faster adoption of AI in safety-critical applications due to enhanced and more accessible robustness guarantees.
Increased trust in AI systems could lead to broader societal integration and legal frameworks acknowledging certified AI reliability.
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Read at arXiv cs.LG