6 papers
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples
Kyle Stein, Arash Mahyari, Guillermo Francia +1
As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly…
Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify o…
Visual Adaptive Prompting for Compositional Zero-Shot Learning
Kyle Stein, Arash Mahyari, Guillermo Francia +1
Vision-Language Models (VLMs) have demonstrated impressive multimodal capabilities in learning joint representations of visual and textual data, making them powerful tools for task…
Transductive One-Shot Learning Meet Subspace Decomposition
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
One-shot learning focuses on adapting pretrained models to recognize newly introduced and unseen classes based on a single labeled image. While variations of few-shot and zero-shot…
Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +1
Integrating new class information without losing previously acquired knowledge remains a central challenge in artificial intelligence, often referred to as catastrophic forgetting.…
Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +1
Backdoor attacks pose a critical threat by embedding hidden triggers into inputs, causing models to misclassify them into target labels. While extensive research has focused on mit…