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- Multi-attentional Deepfake Detection995
A new multi-attentional deepfake detection network that consists of three key components: multiple spatial attention heads to make the network attend to different local parts, a new regional independence loss and an attention guided data augmentation strategy, and state-of-the-art performance.
- Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain629
A novel Spatial-Phase Shallow Learning (SPSL) method is presented, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery to improve the transferability and theoretically analyze the validity of utilizing the phase spectrum.
- Deepfacelab: Integrated, flexible and extensible face-swapping framework379
DeepFaceLab, the current dominant deepfake framework for face-swapping, is presented and its pipeline, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose, is introduced.
- Model Watermarking for Image Processing Networks170
The first model watermarking framework for protecting image processing models, which can resist surrogate models learned with different network structures and objective functions is proposed and is easy to be extended to protect data and traditional image processing algorithms.
- HairCLIP: Design Your Hair by Text and Reference Image147
This paper proposes a new hair editing interaction mode, which enables manipulating hair attributes individually or jointly based on the texts or reference images provided by users, and encode the image and text conditions in a shared embedding space and proposes a unified hair editing framework.
- FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos105
A frame inference-based detection framework (FInfer) to solve the problem of high-visual-quality Deepfake detection by first learning the referenced representations of the current and future frames’ faces and utilizing an autoregressive model.
- A Robust Approach to Multimodal Deepfake Detection101
A novel approach for detecting deepfake video sequences by leveraging data multimodality that extracts audio-visual features from the input video over time and analyzes them using time-aware neural networks, and indicates that a multimodal approach is more effective than a monomodal one, even if trained on disjoint monommodal datasets.
- Initiative Defense against Facial Manipulation90
A novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users is proposed, which is to actively inject imperceptible venom into target facial data before manipulation.
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- Improved Image Matting via Real-time User Clicks and Uncertainty Estimation42
An improved deep image matting framework which is trimap-free and only needs several user click interactions to eliminate the ambiguity is proposed, and a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module is introduced.
- Adversarial batch image steganography against CNN-based pooled steganalysis37
This paper proposes a general framework of adversarial embedding for batch steganography, in which a new loss function is designed and the batch strategy is combined with adversarialembedding, and shows the proposed method significantly improves the security performance of batch Steganography against pooled steganalysis and keeps a high-security level against single image Steganalysis.
- Defining Joint Distortion for JPEG Steganography37
This paper inspects the embedding change from the spatial domain and proposes a principle of Block Boundary Continuity (BBC) for defining JPEG joint distortion, which aims to restrain blocking artifacts caused by inter-block adjacent modifications and thus effectively preserve the spatial continuity at block boundaries.
- HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending31
Besides the unprecedented user interaction mode support, quantitative and qualitative experiments demonstrate the superiority of HairCLIPv2 in terms of editing effects, irrelevant attribute preservation and visual naturalness.
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- GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models23
This work constructs GenderPair, a novel pair-based benchmark designed to assess gender bias in LLMs comprehensively, and establishes pioneering criteria for gender equality benchmarks, spanning dimensions such as inclusivity, diversity, explainability, objectivity, robustness, and realisticity.
- CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design21
A novel approach for indoor scene synthesis, which learns to arrange decomposed cuboid primitives to represent 3D objects within a scene, using an autoregressive model to sequentially arrange cuboids, producing physically plausible scenes.
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- Self-appreciation is not enough: exercise identity mediates body appreciation and physical activity and the role of perceived stress17
The results demonstrated that body appreciation positively predicted physical activity, exercise identity partially mediated the positive effect of body appreciation on physical activity, and perceived stress played a moderating role in body appreciation and exercise identity.
- A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs17
This fusion model is the first to integrate automatic dental segmentation, numbering, and state assessment of teeth on panoramic radiographs, and provides highly accurate results, including detailed visualizations and automated report generation.
- BiFPro: A Bidirectional Facial-data Protection Framework against DeepFake17
Experimental results demonstrate that the proposed Bidirectional Facial-data Protection Framework could generate the watermarking which is fragile to FOD and at the same time robust to FIF with an average watermark extraction success rate reaching more than 95% when defending against the four advanced DeepFake techniques.
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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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