Jul 14, 2020 · Adversarial examples are a type of attack on machine learning (ML) systems which cause misclassification of inputs.

This intriguing property sheds light on using coupling strategies to better detect and reject adversarial examples. We evaluate our rectified rejection (RR) ...

In this paper, we define a metric for quantizing the vulnerability or susceptibility of a given ML model to adversarial manipulation using only properties ...

Dec 7, 2022 · Abstract. Adversarial defenses are naturally evaluated on their ability to tolerate adversarial attacks. To test defenses, diverse ad-.

Aug 26, 2021 · Using similarity metrics to see which attacks change the images the most. For classification neural network, an adversarial example is an input ...

Dec 19, 2021 · The paper focus on adversarial sample attacks which are inputs produced by adding often unnoticeable perturbations to a machine learning model ...

Jul 6, 2021 · In this paper, we propose several metrics to measure robustness of classifiers to natural adversarial examples, and methods to evaluate them.

Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art ...

This method serves as an additional confidence metric to detect adversarial examples in a real-world setting. We conduct the detection experiment for mis ...

Mar 15, 2024 · In this work, we comprehensively survey and present the latest research on attacks based on adversarial examples against deep learning-based cybersecurity ...