2 citations · 2 across the 8 of their papers we have counts for
10 papers
Learning to Watermark Speech Synthesis Against Model-Driven Reconstruction
Weizhi Liu, Yue Li, Hui Tian +1
Modern TTS systems increasingly generate synthetic speech at scale for diverse users. This setting calls for content-level provenance that can verify the origin of released speech…
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,…
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…
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…
SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation
Yue Li, Weizhi Liu, Dongdong Lin
The accelerated advancement of speech generative models has given rise to security issues, including model infringement and unauthorized abuse of content. Although existing generat…
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…
Protecting Your Voice: Temporal-aware Robust Watermarking
Yue Li, Weizhi Liu, Dongdong Lin +2
The rapid advancement of generative models has led to the synthesis of real-fake ambiguous voices. To erase the ambiguity, embedding watermarks into the frequency-domain features o…
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…
GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis
Weizhi Liu, Yue Li, Dongdong Lin +2
Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake…