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Adversarial Detection from Derived Models - World Scientific Publishing
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In this work, we propose a new adversarial detection method called Adversarial Detection from Derived Models (ADDM), which applies derived models to “simulate” ...
Dec 30, 2023 · In this work, we propose a new adversarial detection method called Adversarial Detection from Derived Models (ADDM), which applies derived ...
Dec 9, 2024 · In this work, we propose a new adversarial detection method called Adversarial Detection from Derived Models (ADDM), which applies derived ...
As a defense strategy against adversarial attacks, adversarial detection aims to identify and filter out adversarial data from the data flow based on ...
Nov 27, 2023 · We develop three detection strategies for adversarial examples by analysing differences in the prediction of the surrogate and the CNN model.
We propose an AutoEncoder-based Adversarial Examples (AEAE) detector that can guard DNN models by detecting adversarial examples with low computation in an ...
Feb 24, 2019 · Adversarial samples are detected by measuring the distance between the prediction vectors of the original seed input and each of the squeezed ...
We proac- tively predict adversarial samples based on a set of local interpreters of the target detection model. The robustness of ML models is then improved ...
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Apr 30, 2022 · In this paper, we propose a universal detection framework for adversarial examples and fake images. We observe some differences in the distribution of model ...
Missing: Derived | Show results with:Derived
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