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Found 5 papers, 1 papers with code
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems
In this work, we present an algorithm to distill the policy from a deep Q-network into a compact neuro-fuzzy controller.
Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks.
Inline Detection of DGA Domains Using Side Information
We found that the DGA classifiers that rely on both the domain name and side information have high performance and are more robust against adversaries.
CharBot: A Simple and Effective Method for Evading DGA Classifiers
In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM. MI (a deep learning approach).
Lower bounds on the robustness to adversarial perturbations
The input-output mappings learned by state-of-the-art neural networks are significantly discontinuous.
