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Decreasing Model Stealing Querying for Black Box Adversarial Attacks
Adversarial Attack and Training on Deep Learning-based Gaze estimation
Black-box Adversarial Attacks using Substitute models: Effects of Data Distributions on Sample Transferability
A deep dive into the robustness of AdaBoost Ensembling combined with Adversarial Training
Using object-specific frequency information from labeled data to improve a CNN’s robustness to adversarial attacks
Effect of parameter tuning on reducing the number of queries required to perform model stealing
Are BERT-based fact-checking models robust against adversarial attack?
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