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Found 9 papers, 2 papers with code
PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails
More recent LLMs often incorporate an additional layer of defense, a Guard Model, which is a second LLM that is designed to check and moderate the output response of the primary LLM.
Why Train More? Effective and Efficient Membership Inference via Memorization
Many practical black-box MIAs require query access to the data distribution (the same distribution where the private data is drawn) to train shadow models.
Identifying and Mitigating the Security Risks of Generative AI
However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks.
Rethinking Diversity in Deep Neural Network Testing
We note that the objective of testing DNNs is specific and well-defined: identifying inputs that lead to misclassifications.
Stratified Adversarial Robustness with Rejection
We theoretically analyze the stratified rejection setting and propose a novel defense method -- Adversarial Training with Consistent Prediction-based Rejection (CPR) -- for building a robust selective classifier.
Concept-Based Explanations for Tabular Data
The interpretability of machine learning models has been an essential area of research for the safe deployment of machine learning systems.
Concept-based Explanations for Out-Of-Distribution Detectors
Based on these metrics, we propose an unsupervised framework for learning a set of concepts that satisfy the desired properties of high detection completeness and concept separability, and demonstrate its effectiveness in providing concept-based explanations for diverse off-the-shelf OOD detectors.
Revisiting Adversarial Robustness of Classifiers With a Reject Option
Motivated by this metric, we propose novel loss functions and a robust training method -- \textit{stratified adversarial training with rejection} (SATR) -- for a classifier with reject option, where the goal is to accept and correctly-classify small input perturbations, while allowing the rejection of larger input perturbations that cannot be correctly classified.
Stochastic Doubly Robust Gradient
When training a machine learning model with observational data, it is often encountered that some values are systemically missing.
