8 papers
Diffusion-Guided Adversarial Perturbation Injection for Generalizable Defense Against Facial Manipulations
Yue Li, Linying Xue, Kaiqing Lin +5
Recent advances in GAN and diffusion models have significantly improved the realism and controllability of facial deepfake manipulation, raising serious concerns regarding privacy,…
TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability
Yue Li, Weizhi Liu, Kaiqing Lin +2
Diffusion-based speech generation has achieved remarkable fidelity, increasing the risk of misuse and unauthorized redistribution. However, most existing generative speech watermar…
VocBulwark: Towards Practical Generative Speech Watermarking via Additional-Parameter Injection
Weizhi Liu, Yue Li, Zhaoxia Yin
Generated speech achieves human-level naturalness but escalates security risks of misuse. However, existing watermarking methods fail to reconcile fidelity with robustness, as they…
An Efficient Watermarking Method for Latent Diffusion Models via Low-Rank Adaptation and Dynamic Loss Weighting
Dongdong Lin, Yue Li, Benedetta Tondi +3
The rapid proliferation of Deep Neural Networks (DNNs) is driving a surge in model watermarking technologies, as the trained models themselves constitute valuable intellectual prop…
Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization
Haoxin Yang, Yihong Lin, Jingdan Kang +4
Face anonymization aims to conceal identity information while preserving non-identity attributes. Mainstream diffusion models rely on inference-time interventions such as negative…
Towards Imperceptible Adversarial Defense: A Gradient-Driven Shield against Facial Manipulations
Yue Li, Linying Xue, Dongdong Lin +3
With the flourishing prosperity of generative models, manipulated facial images have become increasingly accessible, raising concerns regarding privacy infringement and societal tr…