{"title":"人脸变形攻击检测中深度特征的多级融合","authors":"Sushma Venktatesh","doi":"10.1109/ICECCME55909.2022.9987842","DOIUrl":null,"url":null,"abstract":"Face biometric systems are widely deployed in a magnitude of security-related applications, including border control. However, the vulnerability of the Face Recognition Systems(FRS) to various types of attacks is well demonstrated. This work presents a novel approach for face morphing attack detection for a single image scenario by performing a multi-level fusion of deep features. The features are extracted using the pre-trained deep CNNs such as AlexNet, ResNet50. These extracted features are combined at both features and score levels to conclude if the given face image is a morph. The proposed single image Morph Attack Detection (S-MAD) approach is extensively evaluated on the face morphing dataset constructed using five different face morphing generation techniques and three different data mediums. The data mediums including digital, print-scan (re-digitised), print-scan compression (re-digitised and compressed.) Extensive experiments are carried out with intra (same datatype used for training and testing) and inter-evaluation scenarios (cross datatype used for training and testing). Further, the proposed method is compared with the State-Of-The-Art (SOTA) approaches for No reference-based/Single image Morph Attack Detection (S-MAD). The statistical analysis indicates the best performance of the proposed approach in all three different mediums.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multilevel Fusion of Deep Features for Face Morphing Attack Detection\",\"authors\":\"Sushma Venktatesh\",\"doi\":\"10.1109/ICECCME55909.2022.9987842\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Face biometric systems are widely deployed in a magnitude of security-related applications, including border control. However, the vulnerability of the Face Recognition Systems(FRS) to various types of attacks is well demonstrated. This work presents a novel approach for face morphing attack detection for a single image scenario by performing a multi-level fusion of deep features. The features are extracted using the pre-trained deep CNNs such as AlexNet, ResNet50. These extracted features are combined at both features and score levels to conclude if the given face image is a morph. The proposed single image Morph Attack Detection (S-MAD) approach is extensively evaluated on the face morphing dataset constructed using five different face morphing generation techniques and three different data mediums. The data mediums including digital, print-scan (re-digitised), print-scan compression (re-digitised and compressed.) Extensive experiments are carried out with intra (same datatype used for training and testing) and inter-evaluation scenarios (cross datatype used for training and testing). Further, the proposed method is compared with the State-Of-The-Art (SOTA) approaches for No reference-based/Single image Morph Attack Detection (S-MAD). The statistical analysis indicates the best performance of the proposed approach in all three different mediums.\",\"PeriodicalId\":202568,\"journal\":{\"name\":\"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECCME55909.2022.9987842\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECCME55909.2022.9987842","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multilevel Fusion of Deep Features for Face Morphing Attack Detection
Face biometric systems are widely deployed in a magnitude of security-related applications, including border control. However, the vulnerability of the Face Recognition Systems(FRS) to various types of attacks is well demonstrated. This work presents a novel approach for face morphing attack detection for a single image scenario by performing a multi-level fusion of deep features. The features are extracted using the pre-trained deep CNNs such as AlexNet, ResNet50. These extracted features are combined at both features and score levels to conclude if the given face image is a morph. The proposed single image Morph Attack Detection (S-MAD) approach is extensively evaluated on the face morphing dataset constructed using five different face morphing generation techniques and three different data mediums. The data mediums including digital, print-scan (re-digitised), print-scan compression (re-digitised and compressed.) Extensive experiments are carried out with intra (same datatype used for training and testing) and inter-evaluation scenarios (cross datatype used for training and testing). Further, the proposed method is compared with the State-Of-The-Art (SOTA) approaches for No reference-based/Single image Morph Attack Detection (S-MAD). The statistical analysis indicates the best performance of the proposed approach in all three different mediums.