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- A high capacity lossless data hiding scheme for JPEG images150
Experimental results show that the proposed high-capacity reversible data hiding method achieves both high capacity and high image quality.
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- Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection126
This work proposes a novel Critical Forgery Mining framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance, and designs a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner.
- Learning Meta Pattern for Face Anti-Spoofing92
A learnable network to extract Meta Pattern (MP) and a two-stream network to hierarchically fuse the input RGB image and the extracted MP by using the proposed Hierarchical Fusion Module (HFM) are proposed.
- Wavelet domain adaptive visible watermarking91
A wavelet domain visible watermarking is proposed, adaptively determined by the effect of luminance and local spatial characteristics to make the degree of marking applied variable according to the features of host images.
- Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing74
The modality-asymmetric masked autoencoder is proposed for multimodal FAS self-supervised pre-training without costly annotated labels and is able to learn more intrinsic task-aware representation and compatible with modality-agnostic downstream settings.
- An improved VLC-based lossless data hiding scheme for JPEG images66
A lossless data hiding scheme which directly embeds data into the bitstream of JPEG images is presented and can achieve a significant improvement of embedding capacity.
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- Robust Clothing-Invariant Gait Recognition50
A random subspace method (RSM) framework for clothing-invariant gait recognition by combining multiple inductive biases for classification is proposed and can achieve promising performance even for small size training set.
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- S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing With Statistical Tokens46
A generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where the pre-trained Vision Transformer models are adapted for the FAS task, and a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms is proposed.
- Self-Supervised 3D Action Representation Learning With Skeleton Cloud Colorization44
This paper addresses self-supervised 3D action representation learning for skeleton-based action recognition and designs a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data.
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- Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less36
This paper proposes the first rehearsal-free method for Domain Continual Learning of FAS, which deals with catastrophic forgetting and unseen domain generalization problems simultaneously and proposes the Proxy Prototype Contrastive Regularization (PPCR) to constrain the continual learning with previous domain knowledge from the proxy prototypes.
- Using Graph Neural Networks to Improve Generalization Capability of the Models for Deepfake Detection35
This paper proposes a graph neural network-based paradigm that casts deepfake detection as a graph binary classification problem and proposes a dual-branch network to extract node features from both RGB images and their color difference images via the Transformer-based trainable node encoder module (TNEM).
- Exposing image forgery through the detection of contrast enhancement35
The inter-channel correlation introduced by color image interpolation is revealed, and a metric is constructed to measure these correlations, which are useful in distinguishing the original and contrast enhanced images.
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- Gait recognition based on the golden ratio31
The golden ratio-based segmentation method is proposed to reduce the influence of clothing and outperforms other approaches, achieving a 94.76 % recognition rate in various clothing conditions and a rate of 91.53 % when bags are being carried.
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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 2026-10-12. 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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