Robustness Assurance Quotient: Demonstrating Context Matters for AI Performance and ML Security

Abstract

We present a novel approach to developing robust AI in light of context-varying situations. This methodology harnesses a suite of indicators to establish a Robustness Assurance Quotient (RAQ) tailored to address environmentally noisy data while maintaining parity with current standards, namely the Fréchet Inception Distance (FID) metric. While the FID metric successfully indicates the closeness of highly structured data, it is less successful measuring closeness of inherently noisy data. A score for ascertaining the closeness between Generative Adversarial Network (GAN)-simulated, inherently noisy radiofrequnecy data and original GAN signals are proposed by focusing on the subject and context of the image. The RAQ demonstrably improved our GAN’s generated content similarity to original waterfall images of radiofrequency signal. We believe our Robustness Assurance Quotient can have a profound impact on improving the robustness of various AI models, in diverse application domains ultimately reducing the negative impact of noisy data.