6 citations · 11 across the 21 of their papers we have counts for
19 papers · 1 filter
ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
Xiaojing Chen, Xinyu Lu, Changtao Miao +1
AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable AI-generated video detection…
EIRES:Training-free AI-Generated Image Detection via Edit-Induced Reconstruction Error Shift
Wan Jiang, Jing Yan, Xiaojing Chen +4
Diffusion models have recently achieved remarkable photorealism, making it increasingly difficult to distinguish real images from generated ones, raising significant privacy and se…
MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios
Changtao Miao, Yi Zhang, Man Luo +9
Rapid advances in Artificial Intelligence Generated Content (AIGC) have enabled increasingly sophisticated face forgeries, posing a significant threat to social security. However,…
Synergistic Prompting for Robust Visual Recognition with Missing Modalities
Zhihui Zhang, Luanyuan Dai, Qika Lin +5
Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, i…
SafeMap: Robust HD Map Construction from Incomplete Observations
Xiaoshuai Hao, Lingdong Kong, Rong Yin +4
Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMa…
DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios
Changtao Miao, Yi Zhang, Weize Gao +11
Recent advances in AIGC have exacerbated the misuse of malicious deepfake content, making the development of reliable deepfake detection methods an essential means to address this…
Adversarially Robust AI-Generated Image Detection for Free: An Information Theoretic Perspective
Ruixuan Zhang, He Wang, Zhengyu Zhao +4
Rapid advances in Artificial Intelligence Generated Images (AIGI) have facilitated malicious use, such as forgery and misinformation. Therefore, numerous methods have been proposed…
WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks
Ziyuan He, Zhiqing Guo, Liejun Wang +3
Deepfake technology poses increasing risks such as privacy invasion and identity theft. To address these threats, we propose WaveGuard, a proactive watermarking framework that enha…
Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering
Jiabao Guo, Ajian Liu, Yunfeng Diao +5
The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-bas…
SUEDE:Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement
Zuying Xie, Changtao Miao, Ajian Liu +4
Face recognition systems are vulnerable to physical attacks (e.g., printed photos) and digital threats (e.g., DeepFake), which are currently being studied as independent visual tas…
MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction
Xiaoshuai Hao, Yunfeng Diao, Mengchuan Wei +7
Map construction task plays a vital role in providing precise and comprehensive static environmental information essential for autonomous driving systems. Primary sensors include c…
Inclusion 2024 Global Multimedia Deepfake Detection Challenge: Towards Multi-dimensional Face Forgery Detection
Yi Zhang, Weize Gao, Changtao Miao +10
In this paper, we present the Global Multimedia Deepfake Detection held concurrently with the Inclusion 2024. Our Multimedia Deepfake Detection aims to detect automatic image and a…
TASAR: Transfer-based Attack on Skeletal Action Recognition
Yunfeng Diao, Baiqi Wu, Ruixuan Zhang +5
Skeletal sequence data, as a widely employed representation of human actions, are crucial in Human Activity Recognition (HAR). Recently, adversarial attacks have been proposed in t…
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks
Yunfeng Diao, Naixin Zhai, Changtao Miao +4
Recent advancements in image synthesis, particularly with the advent of GAN and Diffusion models, have amplified public concerns regarding the dissemination of disinformation. To a…
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space
Yunfeng Diao, Baiqi Wu, Ruixuan Zhang +3
Skeletal motion plays a pivotal role in human activity recognition (HAR). Recently, attack methods have been proposed to identify the universal vulnerability of skeleton-based HAR(…
Post-train Black-box Defense via Bayesian Boundary Correction
He Wang, Yunfeng Diao
Classifiers based on deep neural networks are susceptible to adversarial attack, where the widely existing vulnerability has invoked the research in defending them from potential t…
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack
Yunfeng Diao, He Wang, Tianjia Shao +4
Human Activity Recognition (HAR) has been employed in a wide range of applications, e.g. self-driving cars, where safety and lives are at stake. Recently, the robustness of skeleto…
Defending Black-box Skeleton-based Human Activity Classifiers
He Wang, Yunfeng Diao, Zichang Tan +1
Skeletal motions have been heavily replied upon for human activity recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR has been identified across a variety…
BASAR:Black-box Attack on Skeletal Action Recognition
Yunfeng Diao, Tianjia Shao, Yong-Liang Yang +2
Skeletal motion plays a vital role in human activity recognition as either an independent data source or a complement. The robustness of skeleton-based activity recognizers has bee…