99 citations · 361 across the 41 of their papers we have counts for
43 papers · 1 filter
ForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localization
Lei Xu, Haowei Wang, Shen Chen +3
Multi-modal Large Language Models (MLLMs) offer powerful reasoning for forensic tasks, yet existing approaches utilizing exogenous segmentation decoders often suffer from suboptima…
Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models
Kaiqing Lin, Zhiyuan Yan, Ruoxin Chen +8
Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding. As AI-generated images become realistic, semantic-leve…
GenShield: Unified Detection and Artifact Correction for AI-Generated Images
Zhipei Xu, Xuanyu Zhang, Youmin Xu +5
Diffusion-based image synthesis has made AI-generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applications such as misinformation d…
VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection
Hui Han, Shunli Wang, Yandan Zhao +2
In Deepfake Detection (DFD) tasks, researchers proposed two types of MLLM-based methods: complementary combination with small DFD detectors, or static forgery knowledge injection.…
TripleFDS: Triple Feature Disentanglement and Synthesis for Scene Text Editing
Yuchen Bao, Yiting Wang, Wenjian Huang +5
Scene Text Editing (STE) aims to naturally modify text in images while preserving visual consistency, the decisive factors of which can be divided into three parts, i.e., text styl…
Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection
Kaiqing Lin, Zhiyuan Yan, Ruoxin Chen +7
Detecting AI-generated images with multimodal large language models (MLLMs) has gained increasing attention, due to their rich world knowledge, common-sense reasoning, and potentia…
Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes
Kaiqing Lin, Zhiyuan Yan, Ke-Yue Zhang +7
Securing personal identity against deepfake attacks is increasingly critical in the digital age, especially for celebrities and political figures whose faces are easily accessible…
Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable
Ruoxin Chen, Junwei Xi, Zhiyuan Yan +8
Existing detectors are often trained on biased datasets, leading to the possibility of overfitting on non-causal image attributes that are spuriously correlated with real/synthetic…
Towards General Visual-Linguistic Face Forgery Detection(V2)
Ke Sun, Shen Chen, Taiping Yao +5
Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust. Recent works demonstrate that leveraging multimodal mod…
Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing
Xinghe Fu, Zhiyuan Yan, Taiping Yao +2
The generalization problem is broadly recognized as a critical challenge in detecting deepfakes. Most previous work believes that the generalization gap is caused by the difference…
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Zhiyuan Yan, Jiangming Wang, Peng Jin +7
AI-generated images (AIGIs), such as natural or face images, have become increasingly important yet challenging. In this paper, we start from a new perspective to excavate the reas…
A Quality-Centric Framework for Generic Deepfake Detection
Wentang Song, Zhiyuan Yan, Yuzhen Lin +6
Detecting AI-generated images, particularly deepfakes, has become increasingly crucial, with the primary challenge being the generalization to previously unseen manipulation method…
Decoupled Data Augmentation for Improving Image Classification
Ruoxin Chen, Zhe Wang, Ke-Yue Zhang +5
Recent advancements in image mixing and generative data augmentation have shown promise in enhancing image classification. However, these techniques face the challenge of balancing…
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion
Ke Sun, Shen Chen, Taiping Yao +4
The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often str…
Standing on the Shoulders of Giants: Reprogramming Visual-Language Model for General Deepfake Detection
Kaiqing Lin, Yuzhen Lin, Weixiang Li +2
The proliferation of deepfake faces poses huge potential negative impacts on our daily lives. Despite substantial advancements in deepfake detection over these years, the generaliz…
Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
Zhiyuan Yan, Yandan Zhao, Shen Chen +5
Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to…
Rank-based No-reference Quality Assessment for Face Swapping
Xinghui Zhou, Wenbo Zhou, Tianyi Wei +5
Face swapping has become a prominent research area in computer vision and image processing due to rapid technological advancements. The metric of measuring the quality in most face…
DF40: Toward Next-Generation Deepfake Detection
Zhiyuan Yan, Taiping Yao, Shen Chen +8
We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation. Predominantly, existing works identify top-notch detection al…
Test-Time Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +3
Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems against presentation attacks. While domain generalization (DG) methods have been developed to enhance…
Contrastive Pseudo Learning for Open-World DeepFake Attribution
Zhimin Sun, Shen Chen, Taiping Yao +4
The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken es…
Continual Face Forgery Detection via Historical Distribution Preserving
Ke Sun, Shen Chen, Taiping Yao +3
Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall sh…
Towards General Visual-Linguistic Face Forgery Detection
Ke Sun, Shen Chen, Taiping Yao +4
Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which us…
Instance-Aware Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +4
Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain…
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
Zexin Li, Bangjie Yin, Taiping Yao +4
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information…