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Found 4 papers, 2 papers with code
Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks
This paper presents a novel fully-agnostic framework, DIVA (Detecting InVisible Attacks), that detects attacks solely relying on analyzing the potentially poisoned data set.
Fast Adversarial Label-Flipping Attack on Tabular Data
To demonstrate this risk is inherited in the adversary's objective, we propose FALFA (Fast Adversarial Label-Flipping Attack), a novel efficient attack for crafting adversarial labels.
Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments
Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework.
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain.
