19 citations · 34 across the 8 of their papers we have counts for
8 papers
Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning
Ajian Liu, Haocheng Yuan, Xiao Guo +13
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these…
Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization
Changtao Miao, Qi Chu, Tao Gong +6
With the advancement of face manipulation technology, forgery images in multi-face scenarios are gradually becoming a more complex and realistic challenge. Despite this, detection…
Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding
Jiazhen Wang, Bin Liu, Changtao Miao +4
AI-synthesized text and images have gained significant attention, particularly due to the widespread dissemination of multi-modal manipulations on the internet, which has resulted…
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
Changtao Miao, Qi Chu, Zhentao Tan +7
As deepfake content proliferates online, advancing face manipulation forensics has become crucial. To combat this emerging threat, previous methods mainly focus on studying how to…
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
Wanyi Zhuang, Qi Chu, Zhentao Tan +5
Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-l…
Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning
Wanyi Zhuang, Qi Chu, Haojie Yuan +3
Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative f…