With the increase in applications of face verification, increasing attention has been paid to their accuracy and security. To ensure both the accuracy and safety of these systems, this paper proposes an encrypted face-verification system. In this paper, face features are extracted using deep neural networks and then encrypted with the Paillier algorithm and saved in a data set. The framework of the whole system involves three parties: the client, data server, and verification server. The data server saves the encrypted user features and user ID, the verification server performs verification, and the client is responsible for collecting a requester's information and sending it to the servers. The information is transmitted among parties as cipher text, which means that no parties know the private keys except for the verification server. The proposed scheme is tested with two deep convolutional neural networks architectures on the labeled faces in the Wild and Faces94 data sets. The extensive experimental results, including results for identification and verification tasks, show that our approach can enhance the security of a recognition system with little decrease in accuracy. Therefore, the proposed system is efficient with respect to both the security and high verification accuracy.

Due to the existence of unfavorable factors such as turbid water quality and target occlusion, it is difficult to obtain valid data of target features. Due to the repeated calculation of similar data, the real-time performance of the algorithm is poor. In view of the above problems, this paper proposes a multi-AUV collaborative target recognition method based on transfer-reinforcement learning. The features of the target information which is collected by multi-AUV are fused based on wavelet transformation and affine invariance. The similarity of features is calculated by Mahalanobis distance and the learning model is selected autonomously based on the similarity threshold. Based on the Q-learning reinforcement learning model, the target information under the interference environment is trained intensively, and the effective features are extracted and stored in the source domain, which can reduce the impact of the environmental interference on the target recognition. The feature transfer learning model based on deep confidence network transfers the feature data of the source domain to the target domain, reducing the repeated calculation of similar data, and then ensuring the real-time performance of the algorithm. Simulation experiments are conducted in the SUN dataset under five underwater environments (turbid water, target occlusion, insufficient light, complex background, and overlapping targets), and the results demonstrate that the proposed model achieves better performance.

Introduction: Despite advancements in face anti-spoofing technology, attackers continue to pose challenges with their evolving deceptive methods. This is primarily due to the increased complexity of their attacks, coupled with a diversity in presentation modes, acquisition devices, and prosthetic materials. Furthermore, the scarcity of negative sample data exacerbates the situation by causing domain shift issues and impeding robust generalization. Hence, there is a pressing need for more effective cross-domain approaches to bolster the model’s capability to generalize across different scenarios.Methods: This method improves the effectiveness of face anti-spoofing systems by analyzing pseudo-negative sample features, expanding the training dataset, and boosting cross-domain generalization. By generating pseudo-negative features with a new algorithm and aligning these features with the use of KL divergence loss, we enrich the negative sample dataset, aiding the training of a more robust feature classifier and broadening the range of attacks that the system can defend against.Results: Through experiments on four public datasets (MSU-MFSD, OULU-NPU, Replay-Attack, and CASIA-FASD), we assess the model’s performance within and across datasets by controlling variables. Our method delivers positive results in multiple experiments, including those conducted on smaller datasets.Discussion: Through controlled experiments, we demonstrate the effectiveness of our method. Furthermore, our approach consistently yields favorable results in both intra-dataset and cross-dataset evaluations, thereby highlighting its excellent generalization capabilities. The superior performance on small datasets further underscores our method’s remarkable ability to handle unseen data beyond the training set.

With their growing popularity and widespread applications, face recognition systems are attracting more attention from attackers. Thus, face presentation attack detection has emerged as an important research topic in recent years. Existing methods for face presentation attack detection are affected by different cameras and display devices, and their performance is degraded in cross-database testing. In this paper, we propose a face presentation attack detection scheme that fuses multi-perspective dynamic features. One feature is the globally extracted temporal motion pattern of a face in a video. This involves mapping the local and global motion information of the face in the video into a single image. The motion patterns of genuine and fake faces are different, and these patterns are independent of cameras and display devices. Another feature is the visual rhythm of noise patterns, which differs significantly between single and secondary imaging. The proposed scheme fuses these two features at the decision level. Cross-database tests were conducted among the CASIA-FASD, MSU-MFSD and Replay-Attack databases. The experimental results show that the proposed scheme outperforms state-of-the-art algorithms.