7 citations · 7 across the 17 of their papers we have counts for
17 papers
SoK: Unlearnability and Unlearning for Model Dememorization
Mengying Zhang, Derui Wang, Ruoxi Sun +3
Advanced model dememorization methods, including availability poisoning (unlearnability) and machine unlearning, are emerging as key safeguards against data misuse in machine learn…
E2E-VGuard: Adversarial Prevention for Production LLM-based End-To-End Speech Synthesis
Zhisheng Zhang, Derui Wang, Yifan Mi +6
Recent advancements in speech synthesis technology have enriched our daily lives, with high-quality and human-like audio widely adopted across real-world applications. However, mal…
ALMGuard: Safety Shortcuts and Where to Find Them as Guardrails for Audio-Language Models
Weifei Jin, Yuxin Cao, Junjie Su +5
Recent advances in Audio-Language Models (ALMs) have significantly improved multimodal understanding capabilities. However, the introduction of the audio modality also brings new a…
Mirage Fools the Ear, Mute Hides the Truth: Precise Targeted Adversarial Attacks on Polyphonic Sound Event Detection Systems
Junjie Su, Weifei Jin, Yuxin Cao +3
Sound Event Detection (SED) systems are increasingly deployed in safety-critical applications such as industrial monitoring and audio surveillance. However, their robustness agains…
Failures to Surface Harmful Contents in Video Large Language Models
Yuxin Cao, Wei Song, Derui Wang +2
Video Large Language Models (VideoLLMs) are increasingly deployed on numerous critical applications, where users rely on auto-generated summaries while casually skimming the video…
SafeSpeech: Robust and Universal Voice Protection Against Malicious Speech Synthesis
Zhisheng Zhang, Derui Wang, Qianyi Yang +6
Speech synthesis technology has brought great convenience, while the widespread usage of realistic deepfake audio has triggered hazards. Malicious adversaries may unauthorizedly co…
Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems
Weifei Jin, Yuxin Cao, Junjie Su +6
The widespread application of automatic speech recognition (ASR) supports large-scale voice surveillance, raising concerns about privacy among users. In this paper, we concentrate…
Bones of Contention: Exploring Query-Efficient Attacks against Skeleton Recognition Systems
Yuxin Cao, Kai Ye, Derui Wang +4
Skeleton action recognition models have secured more attention than video-based ones in various applications due to privacy preservation and lower storage requirements. Skeleton da…
Mitigating Unauthorized Speech Synthesis for Voice Protection
Zhisheng Zhang, Qianyi Yang, Derui Wang +4
With just a few speech samples, it is possible to perfectly replicate a speaker's voice in recent years, while malicious voice exploitation (e.g., telecom fraud for illegal financi…
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
Yuxin Cao, Yumeng Zhu, Derui Wang +4
Face recognition pipelines have been widely deployed in various mission-critical systems in trust, equitable and responsible AI applications. However, the emergence of adversarial…
QUEEN: Query Unlearning against Model Extraction
Huajie Chen, Tianqing Zhu, Lefeng Zhang +4
Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and usingthe query r…
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer
Weifei Jin, Yuxin Cao, Junjie Su +5
In light of the widespread application of Automatic Speech Recognition (ASR) systems, their security concerns have received much more attention than ever before, primarily due to t…
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Yuxin Cao, Jinghao Li, Xi Xiao +5
Previous work has shown that well-crafted adversarial perturbations can threaten the security of video recognition systems. Attackers can invade such models with a low query budget…
Reinforcement Unlearning
Dayong Ye, Tianqing Zhu, Congcong Zhu +6
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners. However, one i…
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
Yuxin Cao, Ziyu Zhao, Xi Xiao +3
Video recognition systems are vulnerable to adversarial examples. Recent studies show that style transfer-based and patch-based unrestricted perturbations can effectively improve a…
Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
Bingbing Song, Derui Wang, Tianwei Zhang +3
As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune…
Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection
Yuxin Cao, Yian Li, Yumeng Zhu +2
Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most…