7 papers
DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Yisu Liu, Chenxing Li, Wanqian Zhang +6
Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and…
AutoPrompt: Automated Red-Teaming of Text-to-Image Models via LLM-Driven Adversarial Prompts
Yufan Liu, Wanqian Zhang, Huashan Chen +4
Despite rapid advancements in text-to-image (T2I) models, their safety mechanisms are vulnerable to adversarial prompts, which maliciously generate unsafe images. Current red-teami…
Uneven Event Modeling for Partially Relevant Video Retrieval
Sa Zhu, Huashan Chen, Wanqian Zhang +4
Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments, wherein event modeling is crucial for partitioning the…
Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment
Yufan Liu, Wanqian Zhang, Dayan Wu +3
Model inversion (MI) attack reconstructs the private training data of a target model given its output, posing a significant threat to deep learning models and data privacy. On one…
RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining
Yufan Liu, Jinyang An, Wanqian Zhang +5
The remarkable development of text-to-image generation models has raised notable security concerns, such as the infringement of portrait rights and the generation of inappropriate…
XNN: Paradigm Shift in Mitigating Identity Leakage within Cloud-Enabled Deep Learning
Kaixin Liu, Huixin Xiong, Bingyu Duan +4
In the domain of cloud-based deep learning, the imperative for external computational resources coexists with acute privacy concerns, particularly identity leakage. To address this…