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Found 8 papers, 2 papers with code
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations
Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting.
Adversarial Example Detection Using Latent Neighborhood Graph
We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an input example to determine if the input example is adversarial.
Robust and Accurate Authorship Attribution via Program Normalization
To address these emerging issues, we formulate this security challenge into a general threat model, the $\textit{relational adversary}$, that allows an arbitrary number of the semantics-preserving transformations to be applied to an input in any problem space.
Robustness for Non-Parametric Classification: A Generic Attack and Defense
To test our defense, we provide a novel attack that applies to a wide range of non-parametric classifiers.
An Investigation of Data Poisoning Defenses for Online Learning
Data poisoning attacks -- where an adversary can modify a small fraction of training data, with the goal of forcing the trained classifier to high loss -- are an important threat for machine learning in many applications.
Data Poisoning Attacks against Online Learning
While there has been much prior work on data poisoning, most of it is in the offline setting, and attacks for online learning, where training data arrives in a streaming manner, are not well understood.
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Our analysis shows that its robustness properties depend critically on the value of k - the classifier may be inherently non-robust for small k, but its robustness approaches that of the Bayes Optimal classifier for fast-growing k. We propose a novel modified 1-nearest neighbor classifier, and guarantee its robustness in the large sample limit.
Pufferfish Privacy Mechanisms for Correlated Data
Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient.
