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Found 4 papers, 2 papers with code
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
Further, we show how our CL does not necessarily need to be integrated at training time, as it can be also used as a guardrail at inference time, still producing some improvements in the overall performance of the models.
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks
Natural Language Processing (NLP) models based on Machine Learning (ML) are susceptible to adversarial attacks -- malicious algorithms that imperceptibly modify input text to force models into making incorrect predictions.
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks
While the literature on security attacks and defense of Machine Learning (ML) systems mostly focuses on unrealistic adversarial examples, recent research has raised concern about the under-explored field of realistic adversarial attacks and their implications on the robustness of real-world systems.
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space
We propose a unified framework to generate feasible adversarial examples that satisfy given domain constraints.
