1 citations · 1 across the 6 of their papers we have counts for
7 papers
Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection
Miao He, Peng Cheng, Zhongjie Ba +4
Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve s…
Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues
Zhongjie Ba, Liang Yi, Peng Cheng +3
Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect th…
HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection
Qing Wen, Haohao Li, Zhongjie Ba +4
Advances in AIGC technologies have enabled the synthesis of highly realistic audio deepfakes capable of deceiving human auditory perception. Although numerous audio deepfake detect…
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features
Peng Cheng, Yuwei Wang, Peng Huang +5
Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial att…
Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
Zhongjie Ba, Qingyu Liu, Zhenguang Liu +4
Deepfake technology has given rise to a spectrum of novel and compelling applications. Unfortunately, the widespread proliferation of high-fidelity fake videos has led to pervasive…
Phoneme-Based Proactive Anti-Eavesdropping with Controlled Recording Privilege
Peng Huang, Yao Wei, Peng Cheng +5
The widespread smart devices raise people's concerns of being eavesdropped on. To enhance voice privacy, recent studies exploit the nonlinearity in microphone to jam audio recorder…
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
Xinyu Zhang, Qingyu Liu, Zhongjie Ba +5
Federated Learning (FL) is a promising distributed learning approach that enables multiple clients to collaboratively train a shared global model. However, recent studies show that…