Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

While state-of-the-art diffusion models (DMs) excel in image generation, concerns regarding
their security persist. Earlier research highlighted DMs' vulnerability to backdoor attacks, but …

Dataset sanitization is a widely adopted proactive defense against poisoning-based
backdoor attacks, aimed at filtering out and removing poisoned samples from training …

Personalized diffusion models have gained popularity for adapting pre-trained text-to-image
models to generate images of specific topics with only a few images. However, recent …

The escalating sophistication of cyberattacks has encouraged the integration of machine
learning techniques in intrusion detection systems, but the rise of adversarial examples …

The rapid progress of Large Models (LMs) has recently revolutionized various fields of deep
learning with remarkable grades, ranging from Natural Language Processing (NLP) to …

Backdoor defense is crucial to ensure the safety and robustness of machine learning models
when under attack. However, most existing methods specialize in either the detection or …

In adversarial defense, adversarial purification can be viewed as a special generation task
with the purpose to remove adversarial attacks and diffusion models excel in adversarial …