18 citations · 77 across the 39 of their papers we have counts for
40 papers
MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories
Haojin He, Hao Tan, Zichang Tan +2
Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does not necessarily imply physica…
Marrying Optimal Transport and ODEs for Unified Continuous-Time 4D Reconstruction and Tracking
Liying Yang, Hao Mo, Jialun Liu +7
Existing unified 4D reconstruction and point tracking approaches typically rely on heuristic interpolations or just predict at integer timestamps, lacking kinematic coherence and f…
PointCHR: Point Cloud Analysis via Curvature-Aware Hyperbolic Rectification
Xinxing Yu, Liying Yang, Hao Mo +4
High-curvature regions in 3D point clouds encapsulate critical fine-grained geometric semantics yet exhibit a distinct long-tail sparsity in their spatial distribution. The inheren…
Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection
Hao Tan, Jun Lan, Zichang Tan +7
The growing capability of image generation models has made synthetic images a routine presence in open media, making robust and generalizable AI-Generated Image (AIGI) detection in…
Human4K: A Large-Scale 4K Multi-View Mocap Dataset for Whole-Body 3D Human Reconstruction
Tianshun Han, Ziyu Shi, Lijian Liu +7
Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstabl…
CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection
Haoyuan Zhang, Xiangyu Zhu, Li Gao +3
Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed videos, and 3D masks. D…
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection
Senyuan Shi, Hao Tan, Zichang Tan +4
The rapid evolution of generative models has precipitated a proliferation of fabricated content, posing significant challenges to existing Synthetic Image Detection (SID) methods.…
AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models
Zhiwei Li, Jiacheng Xue, Weining Wang +4
Vision-language models like CLIP have demonstrated remarkable zero-shot transfer capabilities. However, their susceptibility to imperceptible adversarial perturbations remains a cr…
PointCSP: Cross-Sample Semantic Propagation and Stability Preservation in Self-Supervised Point Cloud Learning
Xinxing Yu, Ajian Liu, Sunyuan Qiang +5
Scene-level point cloud self-supervised learning (PC-SSL) has demonstrated potential in enhancing the generalization capability of 3D vision models. Despite the advances in the fie…
StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting
Guoqing Ma, Xun Lin, Hui Ma +6
Most existing Face Forgery Detection (FFD) models assume access to raw face images. In practice, under a client-server framework, private facial data may be intercepted during tran…
PESTalk: Speech-Driven 3D Facial Animation with Personalized Emotional Styles
Tianshun Han, Benjia Zhou, Ajian Liu +4
PESTalk is a novel method for generating 3D facial animations with personalized emotional styles directly from speech. It overcomes key limitations of existing approaches by introd…
Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
Hao Tan, Jun Lan, Zichang Tan +7
Deepfake detection remains a formidable challenge due to the complex and evolving nature of fake content in real-world scenarios. However, existing academic benchmarks suffer from…
MLLM-Enhanced Face Forgery Detection: A Vision-Language Fusion Solution
Siran Peng, Zipei Wang, Li Gao +5
Reliable face forgery detection algorithms are crucial for countering the growing threat of deepfake-driven disinformation. Previous research has demonstrated the potential of Mult…
Your Classifier Can Do More: Towards Balancing the Gaps in Classification, Robustness, and Generation
Kaichao Jiang, He Wang, Xiaoshuai Hao +5
Joint Energy-based Models (JEMs) are well known for their ability to unify classification and generation within a single framework. Despite their promising generative and discrimin…
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-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection
Shunxin Chen, Ajian Liu, Junze Zheng +4
Facial recognition systems in real-world scenarios are susceptible to both digital and physical attacks. Previous methods have attempted to achieve classification by learning a com…
FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection
Yongze Li, Ning Li, Ajian Liu +7
Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical…
Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport
Hao Tan, Zichang Tan, Jun Li +3
Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of p…
Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features
Liying Yang, Chen Liu, Zhenwei Zhu +4
Recently, the generation of dynamic 3D objects from a video has shown impressive results. Existing methods directly optimize Gaussians using whole information in frames. However, w…
La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detection
Hang Zou, Chenxi Du, Hui Zhang +4
Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separa…
Dynamic Analysis and Adaptive Discriminator for Fake News Detection
Xinqi Su, Zitong Yu, Yawen Cui +7
In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection methods can generall…
A Unified Framework for Iris Anti-Spoofing: Introducing Iris Anti-Spoofing Cross-Domain-Testing Protocol and Masked-MoE Method
Hang Zou, Chenxi Du, Ajian Liu +6
Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, iris images captured by different devices exhibit certain and device-r…
Unified Physical-Digital Attack Detection Challenge
Haocheng Yuan, Ajian Liu, Junze Zheng +6
Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existin…
Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues
Xianhua He, Dashuang Liang, Song Yang +7
Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenar…