arXiv:2606.31581v1 Announce Type: new Abstract: We investigate the problem of the robustness of a trained neural network to the perturbation of its input values. More specifically, we examine the interplay between the accuracy of the network, as measured by the mean squared error, and robustness. Accordingly, we present a robustness measure, which, with high probability, suggests an upper bound on the mean squared error of the network, with respect to an input data set, for a given perturbation of the input values of the network. The measure we propose is both simple and efficient to compute,
The increasing deployment of neural networks in critical applications necessitates a deeper understanding of their robustness to adversarial inputs and random perturbations.
Ensuring the reliability and safety of AI systems, especially in high-stakes environments, hinges on quantifiable measures of robustness against unexpected input variations.
This research provides a new, efficient method for quantifying neural network robustness, which can directly influence validation processes and the development of more resilient AI models.
- · AI safety researchers
- · Developers of mission-critical AI systems
- · Industries deploying AI in sensitive applications
- · Developers of unrobust AI models
- · Systems reliant on brittle AI
- · AI applications in highly variable environments without proper robustness checks
Improved understanding and quantification of neural network reliability will become standard practice in AI development.
This could lead to new architectural designs for neural networks that are inherently more robust to input perturbations.
The enhanced trustworthiness of AI systems may accelerate their adoption in highly regulated sectors, potentially enabling new autonomous functionalities.
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Read at arXiv cs.LG