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Claim this profileWorks70 from public data
- 449
- Local Relation Learning for Face Forgery Detection359
This work proposes a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern and an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation, which further improves the reliability of the similarity pattern.
- Dual Contrastive Learning for General Face Forgery Detection260
A novel face forgery detection framework, named Dual Contrastive Learning (DCL), which specially constructs positive and negative paired data and performs designed contrastive learning at different granularities to learn generalized feature representation is proposed.
- Spatiotemporal Inconsistency Learning for DeepFake Video Detection241
A novel temporal modeling paradigm in TIM is presented by exploiting the temporal difference over adjacent frames along with both horizontal and vertical directions and the ISM simultaneously utilizes the spatial information from SIM and temporal information from TIM to establish a more comprehensive spatial-temporal representation.
- Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition204
A unified adversarial face generation method - Adv-Makeup, which can realize imperceptible and transferable attack under the black-box setting, and implements a fine-grained meta-learning based adversarial attack strategy to learn more vulnerable or sensitive features from various models.
- Exploiting Fine-Grained Face Forgery Clues via Progressive Enhancement Learning194
A fine-grained decomposition of RGB images is performed to completely decouple the real and fake traces in the frequency space and this method outperforms the state-of-the-art face forgery detection methods.
- Face Anti-Spoofing via Disentangled Representation Learning155
A novel perspective of face anti-spoofing is proposed that disentangles the liveness features and content features from images, and theLiveness features is further used for classification, and a Convolutional Neural Network architecture is put forward.
- Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing127
This work designs a domain feature based on Instance Normalization and proposes a domain representation learning module (DRLM) to extract discriminative domain features for clustering and designs a generalizable face anti-spoofing with meta-learning to enhance the interpretability through visualization.
- Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection124
This work elaborately design an Intra-Snippet Inconsistency Module (Intra-SIM) and an Inter- Snippet Interaction Module (Inter- SIM) to establish a dynamic inconsistency modelling frame- work and outperforms the state of the art competitors on four popular benchmark dataset.
- Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing118
A novel perspective of face anti-spoofing that focuses on the normalization selection in the feature extraction process is proposed, which adaptively selects feature normalization methods according to the inputs, aiming to learn domain-agnostic and discriminative representation.
- Dual Reweighting Domain Generalization for Face Presentation Attack Detection106
A novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization.
- Instance-Aware Domain Generalization for Face Anti-Spoofing105
Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles by adaptingively eliminate the style-sensitive feature correlation, boosting the generalization.
- Exploring Frequency Adversarial Attacks for Face Forgery Detection101
This paper proposes a frequency adversarial attack method against face forgery detectors that fools not only the spatial-based detectors but also the state-of- the-art frequency-based detector effectively and enhances the transferability across face forgeries detectors as black-box attacks.
- Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing76
An Adaptive Mixture of Experts Learning (AMEL) framework is proposed, which exploits the domain-specific information to adaptively establish the link among the seen source domains and unseen target domains to further improve the generalization.
- Generative Domain Adaptation for Face Anti-Spoofing75
A novel perspective of UDA FAS is proposed that directly fits the target data to the models, and further feeds the stylized data into the well-trained source model for classification, and combines two carefully designed consistency constraints.
- Delving into Data: Effectively Substitute Training for Black-box Attack73
A novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process and the combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack.
- Towards General Visual-Linguistic Face Forgery Detection67
This work proposes Face Forgery Text Generator (FFTG), a novel annotation pipeline that generates accurate text descriptions by leveraging forgery masks for initial region and type identification, followed by a comprehensive prompting strategy to guide MLLMs in reducing hallucination.
- Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition66
Sibling-Attack is presented, a new FR attack technique for the first time that explores a novel multi-task perspective and outperforms state-of-the-art FR attack techniques by a non-trivial margin.
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- Test-Time Domain Generalization for Face Anti-Spoofing63
A novel Test-Time Domain Generalization (TTDG) framework for FAS is introduced, which leverages the testing data to boost the model's generalizability and can be seamlessly integrated into not only the CNN but also ViT backbones.
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- Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection51
A novel approach to incorporate the rich semantic knowledge embedded within large-scale vision foundation models to expand the previous discriminative space (based on forgery patterns only), such that the discrimination is decided by both forgery and semantic cues, thereby reducing the overfitting to specific forgery patterns.
- Structure Destruction and Content Combination for Face Anti-Spoofing48
The former mechanism destroys images into patches to construct a non-structural input, while the latter mechanism recombines patches from different subdomains or classes into a mixup construct based on this splitting-and-splicing operation.
- Contrastive Pseudo Learning for Open-World DeepFake Attribution47
A novel framework named Contrastive Pseudo Learning (CPL) is proposed for the OW-DFA task through introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, and designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set.
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- Continual Face Forgery Detection via Historical Distribution Preserving35
A Historical Distribution Preserving (HDP) framework is proposed that reserves and preserves the distributions of historical faces and uses universal adversarial perturbation to simulate historical forgery distribution and knowledge distillation to maintain the distribution variation of real faces across different models.
- Face Manipulation Detection via Auxiliary Supervision35
A multi-scale framework is proposed to estimate the manipulation probability with texture map and blending boundary as auxiliary supervisions to guide the network to learn discriminative and generalizable cues.
- Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection29
A novel Region-Aware Temporal Filter (RATF) module which automatically generates corresponding temporal filters for different spatial regions and guides the corresponding regions to adaptively learn temporal inconsistency, which greatly enhances the overall representational ability.
- Towards Unified Defense for Face Forgery and Spoofing Attacks via Dual Space Reconstruction Learning28
A dual space reconstruction learning framework that models the commonalities of genuine faces in both spatial and frequency domains and introduces a dynamic filtering module that filters out the redundant information retained by the reconstruction and enhances the critical divergence between the real and the attack to achieve better classification features.
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- Rethinking Open-World DeepFake Attribution with Multi-perspective Sensory Learning21
A novel task named Open-World DeepFake Attribution, and the corresponding benchmark OW-DFA++, which aims to evaluate attribution performance against various types of fake faces in open-world scenarios are introduced and a Multi-Perspective Sensory Learning (MPSL) framework is proposed that aims to address the challenge of OW-DFA++.
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- TF-FAS: Twofold-Element Fine-Grained Semantic Guidance for Generalizable Face Anti-spoofing18
This work proposes a novel framework called TF-FAS, which aims to thoroughly explore and harness twofold-element fine-grained semantic guidance to enhance generalization, and presents a Fine-Grained Categorical Element Module (FCEM) to explore fine-grained categorical element guidance.
- Reciprocal normalization for domain adaptation17
Reciprocal Normalization is proposed, a novel normalization method that outperforms existing normalization counterparts by a large margin and helps state-of-the-art adaptation approaches achieve better results.
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- Decoupled Data Augmentation for Improving Image Classification10
Decoupled Data Augmentation (De-DA), which resolves the fidelity-diversity dilemma by separating images into CDPs and CIPs and handling them adaptively, and replaces the image's CIP with inter-class variants, creating diverse CDP-CIP combinations.
- A Quality-Centric Framework for Generic Deepfake Detection9
A novel quality-centric framework for generic deepfake detection is proposed, which is composed of a Quality Evaluator, a low-quality data enhancement module, and a learning pacing strategy that explicitly incorporates forgery quality into the training process.
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- DITL 2 : Dual-Stage Invariance Transfer Learning for Generalizable Document Image Tampering Localization5
DITL2 is proposed, a Dual-stage Invariance Transfer Learning framework for Document Image Tampering Localization that consists of Cross-Domain Invariance Pre-training (CDIP) and Frequency Decoupling Parameter Adaptation (FDPA).
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- Domain-Hallucinated Updating for Multi-Domain Face Anti-spoofing4
This work proposes a novel Domain-Hallucinated Updating (DHU) framework to facilitate the hallucination of data for updating FAS model and demonstrates that the proposed method outperforms state-of-the-art competitors in terms of effectiveness.
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- Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models3
Deep Visual Residual MLLM (Deep-VRM) is proposed to preserve early semantic processing while injecting artifact-specific visual signals as a residual path into an intermediate layer, where they are fused with semantic token representations and propagated through subsequent trainable layers.
- ForgeryVCR: Visual-Centric Reasoning via Efficient Forensic Tools in MLLMs for Image Forgery Detection and Localization3
This work proposes ForgeryVCR, a framework that incorporates a forensic toolbox to materialize imperceptible traces into explicit visual intermediates via Visual-Centric Reasoning and achieves state-of-the-art (SOTA) performance in both detection and localization tasks, demonstrating superior generalization and robustness with minimal tool redundancy.
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- GenShield: Unified Detection and Artifact Correction for AI-Generated Images1
GenShield is proposed, a unified autoregressive framework that jointly performs explainable AIGI detection and controllable artifact correction in a closed loop from diagnosis to restoration, revealing a mutually reinforcing relationship between these two tasks.
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- ForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localization–
This work proposes ForensicsTok, which reformulates image manipulation localization as an autoregressive sequence generation task, and introduces a Token Splatting Decoder to map tokens to binary masks via codebook-aware code smoothing, which mitigates sharp gradients from deterministic detokenizers.
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- SonarGuard2: Ultrasonic Face Liveness Detection Based on Adaptive Doppler Effect Feature Extraction–
A novel framework, named SonarGuard2, adaptively selecting the ultrasonic signals and analyse the Doppler effect, introduces acoustic echo cancellation to filter to get the Doppler effect features, and utilizes complex convolutional neural networks to enhance the abilitity to model the Doppler effect features.
- Artificial Intelligence Security Competition (AISC)–
The competition rules of these three tracks and the solutions of top-ranking teams in each track are introduced, including Deepfake Security Competition, Autonomous driving security Competition, and Face Recognition Security Competition.
- Multi-person/Group Interactive Video Generation–
This work proposes a novel human motion generation framework which can simultaneously consider the temporal coherence of each individual action, and consists of two components: Semantic Extractor, Motion Generator.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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