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We highlight the importance of using dynamic FR strategies to evaluate AFR methods, and propose DivTrackee as a promising countermeasure.

We explore whether targeted clean-label data poisoning attacks can generalize to diverse target variations

We introduce Generative Adversarial Suffix Prompter (GASP), a novel framework that combines human-readable prompt generation with Latent Bayesian Optimization (LBO) to improve adversarial suffix creation in a fully black-box setting.

We propose a structured neural ODE process model to estimate flux and balance samples using gene-expression time-series data

We propose a general framework to craft invisibility cloak for human pose estimation models

We introduce a Latent Clustering-based Selection method to choose a core subset from the entire unlabeled dataset, aiming to improve the efficiency of self-supervised adversarial training while preserving robustness.

We introduce the Weight-Curvature Index (WCI), a novel metric that captures the interplay between model parameters and loss landscape curvature to better understand and improve adversarially robust generalization in deep learning.

We propose AutoDefense, a response-filtering based multi-agent defense framework that filters harmful responses from LLMs.

Study how to certify and train for cost-sensitive robustness using randomized smoothing.

We propose an availability poisoning attack for generating transferable poisoned data across different victim learners.