most citedLocate and Verify: A Two-Stream Network for Improved Deepfake Detection

2 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2024

FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning

Gaojian Wang, Feng Lin, Tong Wu +3

This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable facial representation that boosts various face security tasks with respect to generaliza…

cs.CR2024

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…

cs.CV2024

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…

cs.CR2024

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…

cs.CR20231 cited

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…

cs.CV20232 cited

Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

Chao Shuai, Jieming Zhong, Shuang Wu +6

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to ima…