With the rapid development and popularity of the Internet, multimedia security has become a
general essential concern. Especially, as manipulation of digital images gets much easier …

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

Watermarking neural networks is a quite important means to protect the intellectual property
(IP) of neural networks. In this paper, we introduce a novel digital watermarking framework …

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 …

The huge training overhead, considerable commercial value, and various potential security
risks make it urgent to protect the intellectual property (IP) of Deep Neural Networks (DNNs) …

Face swapping has drawn a lot of attention for its compelling performance. However, current
deepfake methods suffer the effects of obscure workflow and poor performance. To solve …

Abstract In recent decades, Generative Adversarial Network (GAN) and its variants have
achieved unprecedented success in image synthesis. However, well-trained GANs are …

Recent research shows deep neural networks are vulnerable to different types of attacks,
such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among them …