9 papers
Towards Generalizable Deepfake Detection via Real Distribution Bias Correction
Ming-Hui Liu, Harry Cheng, Xin Luo +2
To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using available source domain data. Ho…
Progressively Exploring and Exploiting Inference Data to Break Fine-Grained Classification Barrier
Li-Jun Zhao, Si-Yuan Zhang, Zhen-Duo Chen +2
Current fine-grained classification research primarily focuses on fine-grained feature learning. However, in real-world scenarios, fine-grained data annotation is challenging, and…
Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment
Hong Han, Hao-Chen Pei, Zhao-Zheng Nie +2
Automatic pronunciation assessment plays a crucial role in computer-assisted pronunciation training systems. Due to the ability to perform multiple pronunciation tasks simultaneous…
Suppressing Gradient Conflict for Generalizable Deepfake Detection
Ming-Hui Liu, Harry Cheng, Xin Luo +1
Robust deepfake detection models must be capable of generalizing to ever-evolving manipulation techniques beyond training data. A promising strategy is to augment the training data…
Learning Real Facial Concepts for Independent Deepfake Detection
Ming-Hui Liu, Harry Cheng, Tianyi Wang +2
Deepfake detection models often struggle with generalization to unseen datasets, manifesting as misclassifying real instances as fake in target domains. This is primarily due to an…
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution
Ming-Hui Liu, Xiao-Qian Liu, Xin Luo +1
Deepfake attribution (DFA) aims to perform multiclassification on different facial manipulation techniques, thereby mitigating the detrimental effects of forgery content on the soc…