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- Lizhuang MaSuggested from co-authorship
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Works160 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.
- 316
- 281
- Federated Learning with Label Distribution Skew via Logits Calibration271
This paper investigates the label distribution skew in FL and proposes FedLC, which calibrates the logits before softmax cross-entropy according to the probability of occurrence of each class, and applies a fine-grained calibrated cross-entropy loss to local update by adding a pairwise label margin.
- 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.
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- 209
- 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.
- HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting127
This work designs a Length-Extendable Patcher (LEP) tailored to MTS, which enriches the boundary information of patches and alleviates semantic incoherence in series, and devise a Hierarchical Dependency Explorer (HDE) based on pure MLPs.
- 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.
- 107
- 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.
- Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning89
This paper investigates whether and how video-level blending can be effective in video and designs a lightweight Spatiotemporal Adapter (StA) to equip a pretrained image model with the ability to capture both spatial and temporal features jointly and efficiently.
- Disentangled High Quality Salient Object Detection89
A novel deep learning framework for high- resolution SOD task is proposed, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN), which achieves superior performance compared to the state-of-the-art methods.
- LaRE 2 : Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection83
This work comes up with the Latent Reconstruction Error (LaRE), the first reconstruction-error based feature in the latent space for generated image detection and proposes an Error-Guided feature REfinement module (EGRE), which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature.
- 83
- Towards Practical Certifiable Patch Defense with Vision Transformer78
A progressive smoothed image modeling task to train Vision Transformer, which can capture the more discriminable local context of an image while preserving the global semantic information and obtains state-of-the-art clean and certified accuracy with inferring efficiently on CIFAR-10 and ImageNet.
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 2026-10-14. 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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