基于多特征融合的3D打印面具攻击检测 下载: 1252次封面文章
ing at the spoofing attacks for the current face authentication systems, the traditional spoofing attacks include displaying printed photos and replaying recorded videos. With the rapid development of three-dimensional (3D) printing technology, the 3D mask spoofing attack is becoming a new threat. On the basis of the shearlet transform and combining with the 3D geometric attributes and the local regional texture changes, a method by utilizing the multilayer autoencoder network to conduct the feature fusion-based classification to identify the attack mask is proposed for the 3D mask spoofing attack. The low-frequency sub-band and several high-frequency sub-bands are extracted from the 3D image of the target face by the non sub-sampled shearlet transform method. The scale space function is used to detect, locate and distribute the feature points and then to generate feature operators in the low-frequency sub-band . Then, the generated feature operators and the texture features extracted from the high-frequency sub-band are combined in series and fed into the stacked autoencoder network and the softmax classifier to conduct the bottleneck feature fusion-based classification. The experimental results in the BFFD database based on the flexible TPU material 3D print mask shows that, the multi-feature fusion method added the 3D geometric feature has an obvious improvement for the accuracy of the anti-spoofing performance against 3D mask attacks to compare with the previous method of using the texture feature alone.
陆经纬, 陈鹤天, 马肖攀, 陈继民. 基于多特征融合的3D打印面具攻击检测[J]. 激光与光电子学进展, 2019, 56(3): 031002. Jingwei Lu, Hetian Chen, Xiaopan Ma, Jimin Chen. 3D Printing Mask Attacks Detection Based on Multi-Feature Fusion[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031002.