Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Diffusion-based generative models have shown great potential for image synthesis, but
there is a lack of research on the security and privacy risks they may pose. In this paper, we …

Text-to-image generative models such as Stable Diffusion and DALL• E raise many ethical
concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones …

Machine learning (ML) has been widely adopted in various privacy-critical applications, eg,
face recognition and medical image analysis. However, recent research has shown that ML …

Code generation language models are trained on billions of lines of source code to provide
code generation and auto-completion features, like those offered by code assistant GitHub …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Federated learning (FL) has recently emerged as a promising distributed machine learning
(ML) paradigm. Practical needs of the" right to be forgotten" and countering data poisoning …

Given a set of unlabeled images or (image, text) pairs, contrastive learning aims to pre-train
an image encoder that can be used as a feature extractor for many downstream tasks. In this …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …