Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include face recognition and deepfake detection.

Research and Interests

• Face Recognition

Publications

• Yufei Liang, Jiangning Zhang, Shiwei Zhao, Runze Wu, Yong Liu, and Shuwen Pan. Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection. IEEE Transactions on Image Processing, 32:4327-4340, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving reconstruction-based method and proposes a novel Omni-frequency Channel-selection Reconstruction (OCR-GAN) network to handle sensory anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-theart 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.11. and the current SOTA method by +0.31.. The source code is available in the additional materials.

  @article{liang2023omni,
  title = {Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection},
  author = {Yufei Liang and Jiangning Zhang and Shiwei Zhao and Runze Wu and Yong Liu and Shuwen Pan},
  year = 2023,
  journal = {IEEE Transactions on Image Processing},
  volume = 32,
  pages = {4327-4340},
  doi = {10.1109/TIP.2023.3293772},
  abstract = {Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving reconstruction-based method and proposes a novel Omni-frequency Channel-selection Reconstruction (OCR-GAN) network to handle sensory anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-theart 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.11. and the current SOTA method by +0.31.. The source code is available in the additional materials.}
  }

• Yufei Liang, Mengmeng Wang, Yining Jin, Shuwen Pan, and Yong Liu. Hierarchical Supervisions with Two-Stream Network for Deepfake Detection. Pattern Recognition Letters, 172:121-127, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Recently, the quality of face generation and manipulation has reached impressive levels, making it diffi-cult even for humans to distinguish real and fake faces. At the same time, methods to distinguish fake faces from reals came out, such as Deepfake detection. However, the task of Deepfake detection remains challenging, especially the low-quality fake images circulating on the Internet and the diversity of face generation methods. In this work, we propose a new Deepfake detection network that could effectively distinguish both high-quality and low-quality faces generated by various generation methods. First, we design a two-stream framework that incorporates a regular spatial stream and a frequency stream to handle the low-quality problem since we find that the frequency domain artifacts of low-quality images will be preserved. Second, we introduce hierarchical supervisions in a coarse-to-fine manner, which con-sists of a coarse binary classification branch to classify reals and fakes and a five-category classification branch to classify reals and four different types of fakes. Extensive experiments have proved the effec-tiveness of our framework on several widely used datasets.

  @article{liang2023hs,
  title = {Hierarchical Supervisions with Two-Stream Network for Deepfake Detection},
  author = {Yufei Liang and Mengmeng Wang and Yining Jin and Shuwen Pan and Yong Liu},
  year = 2023,
  journal = {Pattern Recognition Letters},
  volume = 172,
  pages = {121-127},
  doi = {10.1016/j.patrec.2023.05.029},
  abstract = {Recently, the quality of face generation and manipulation has reached impressive levels, making it diffi-cult even for humans to distinguish real and fake faces. At the same time, methods to distinguish fake faces from reals came out, such as Deepfake detection. However, the task of Deepfake detection remains challenging, especially the low-quality fake images circulating on the Internet and the diversity of face generation methods. In this work, we propose a new Deepfake detection network that could effectively distinguish both high-quality and low-quality faces generated by various generation methods. First, we design a two-stream framework that incorporates a regular spatial stream and a frequency stream to handle the low-quality problem since we find that the frequency domain artifacts of low-quality images will be preserved. Second, we introduce hierarchical supervisions in a coarse-to-fine manner, which con-sists of a coarse binary classification branch to classify reals and fakes and a five-category classification branch to classify reals and four different types of fakes. Extensive experiments have proved the effec-tiveness of our framework on several widely used datasets.}
  }

• Lin Li, Wendong Ding, Yongkun Wen, Yufei Liang, Yong Liu, and Guowei Wan. A Unified BEV Model for Joint Learning 3D Local Features and Overlap Estimation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 8341-8348, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird’s-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.

  @inproceedings{li2023bev,
  title = {A Unified BEV Model for Joint Learning 3D Local Features and Overlap Estimation},
  author = {Lin Li and Wendong Ding and Yongkun Wen and Yufei Liang and Yong Liu and Guowei Wan},
  year = 2023,
  booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {8341-8348},
  doi = {10.1109/ICRA48891.2023.10160492},
  abstract = {Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.}
  }