GradID: Adversarial Detection via Intrinsic Dimensionality of Gradients

This article likely presents a novel method for detecting adversarial attacks on machine learning models. The core idea revolves around analyzing the intrinsic dimensionality of gradients, which could potentially differentiate between legitimate and adversarial inputs. The use of 'ArXiv' as the source indicates this is a pre-print, suggesting the work is recent and potentially not yet peer-reviewed. The focus on adversarial detection is a significant area of research, as it addresses the vulnerability of models to malicious inputs.