基于软件定义的物联网设备安全管理关键技术研究
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1.ProcGuard:Process Injection Behaviours Detection Using Fine-grained Analysis of API Call Chain with Deep Learning
- Juan Wang;Chenjun Ma;Ziang Li;Huanyu Yuan;Jie Wang;
- 《2022 IEEE International Conference on Trust,Security and Privacy in Computing and Communications》
- 2022年
- 会议
2.Measuring Data Reconstruction Defenses in Collaborative Inference Systems
- Mengda Yang;Ziang Li;Juan Wang;Hongxin Hu;Ao Ren;Xiaoyang Xu;Wenzhe Yi;
- 《Conference and Workshop on Neural Information Processing Systems 2022》
- 2022年
- 会议
3.Face anti-spoofing based on dynamic color texture analysis using local directional number pattern
- 关键词:
- Database systems;Fake detection;Textures;Collaborative representations;Color texture analysis;Complete information;Evaluation protocol;Face recognition systems;Illumination variation;Motion information;Spatial temporals
- Zhou, Junwei;Shu, Ke;Liu, Peng;Xiang, Jianwen;Xiong, Shengwu
- 《25th International Conference on Pattern Recognition, ICPR 2020》
- 2020年
- January 10, 2021 - January 15, 2021
- Virtual, Milan, Italy
- 会议
Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel "LDN-TOP representation followed by ProCRC classification" pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN-TOP). The multi-scale LDN-TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive. © 2020 IEEE
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