The recent advances in diffusion models (DMs) have revolutionized the generation of
realistic and complex images. However, these models also introduce potential safety …

Diffusion models (DMs) have achieved remarkable success in text-to-image generation, but
they also pose safety risks, such as the potential generation of harmful content and copyright …

In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed,
pre-trained model at test time. Compared to conventional adversarial defenses, VP allows …

This work presents a comprehensive exploration of Reverse Engineering of Deceptions
(RED) in the field of adversarial machine learning. It delves into the intricacies of machine …

Recent studies have found that medical images are vulnerable to adversarial attacks.
However, it is difficult to protect medical imaging systems from adversarial examples in that …

Adversarial robustness studies the worst-case performance of a machine learning model to
ensure safety and reliability. With the proliferation of deep-learning-based technology, the …

Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and
unreliable performance in the real world. Although adversarial training (AT) is currently one …

The convincing performances of deep neural networks (DNNs) can be degraded
tremendously under malicious samples, known as adversarial examples. Besides, with the …

Deep neural networks have performed remarkably in many areas, including image-related
classification tasks. However, various studies have shown that they are vulnerable to …

Model Parsing defines the research task of predicting hyperparameters of the generative
model (GM), given a generated image as input. Since a diverse set of hyperparameters is …