Generative image modeling enables a wide range of applications but raises ethical
concerns about responsible deployment. This paper introduces an active strategy combining …

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-
generated content (AIGC) has garnered increasing attention and is leading a paradigm shift …

Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
Widespread interest exists in incorporating DMs into downstream applications, such as …

Nowadays, neural networks are actively used for data hiding; however, there is currently no
systematic knowledge regarding their utilization in this field. This is a significant gap …

Recently, the demand for the generation, sharing, and storage of massive amounts of
multimedia information—especially in the form of images—from different intelligent devices …

Abstract Protecting the Intellectual Property Rights (IPR) associated to Deep Neural
Networks (DNNs) is a pressing need pushed by the high costs required to train such …

Despite the tremendous success, deep neural networks are exposed to serious IP
infringement risks. Given a target deep model, if the attacker knows its full information, it can …

Recently, Generative Diffusion Models (GDMs) have showcased their remarkable
capabilities in learning and generating images. A large community of GDMs has naturally …

Abstract Diffusion Models (DMs) have shown remarkable capabilities in various image-
generation tasks. However there are growing concerns that DMs could be used to imitate …

While existing watermarking attack methods can disturb the correct extraction of watermark
information, the visual quality of watermarked images will be greatly damaged. Therefore, a …