Search Results (80)

Biometric authentication using human physiological and behavioral characteristics has been widely adopted, with speaker verification attracting attention due to its convenience and noncontact nature. Conventional speaker verification systems remain vulnerable to spoofing attacks, however, often requiring integration with separate spoofed speech detection models. In this work, the authors propose an emotion-dependent speaker verification system that integrates speaker characteristics with emotional speech characteristics, enhancing robustness against spoofed speech without relying on additional classification models. By comparing acoustic characteristics of emotions between registered and verification speech using pretrained models, the proposed method reduces the equal error rate compared to conventional speaker verification systems, achieving an average equal error rate of 1.13% for speaker verification and 17.7% for the anti-spoofing task. Researchers additionally conducted a user evaluation experiment to assess the usability of emotion-dependent speaker verification. The results indicate that although emotion-dependent authentication was initially cognitively stressful, participants adapted over time, and the burden was significantly reduced after three sessions. Among the tested emotions (anger, joy, sadness, and neutral), sadness proved most effective, with stable scores, a low error rate, and minimal user strain. These findings suggest that neutral speech is not always the optimal choice for speaker verification and that well-designed emotion-dependent authentication can offer a practical and robust security solution. Full article

Achieving reliable navigation is critical for GNSS receivers subject to spoofing attacks. Utilizing the inherent sparsity and inconsistency of spoofing signals, this paper proposes an anti-spoofing framework for GNSS receivers to detect, classify, and recover positions from spoofing attacks without additional devices. A sparse decomposition algorithm with non-negative constraints limited by signal power magnitudes is proposed to achieve accurate spoofing detections while extracting key features of the received signals. In the classification stage, these features continuously refine each channel of the receiver’s code tracking loop, ensuring that it tracks either the authentic or counterfeit signal components. Moreover, by leveraging the inherent inconsistency of spoofing properties, we incorporate the Hausdorff distance to determine the most overlapped position sets, distinguishing genuine trajectories and mitigating spoofing effects. Experiments on the TEXBAT dataset show that the proposed algorithm detects 98% of spoofing attacks, ensuring stable position recovery with an average RMSE of 6.32 m across various time periods. Full article

As voice cloning technology rapidly advances, the risk of personal voices being misused by malicious actors for fraud or other illegal activities has significantly increased, making the collection of speech data increasingly challenging. To address this issue, this study proposes a data augmentation method based on XText-to-Speech (XTTS) synthesis to tackle the challenges of small-sample, multi-class speech recognition, using profanity as a case study to achieve high-accuracy keyword recognition. Two models were therefore evaluated: a CNN model (Proposed-I) and a CNN-Transformer hybrid model (Proposed-II). Proposed-I leverages local feature extraction, improving accuracy on a real human speech (RHS) test set from 55.35% without augmentation to 80.36% with XTTS-enhanced data. Proposed-II integrates CNN’s local feature extraction with Transformer’s long-range dependency modeling, further boosting test set accuracy to 88.90% while reducing the parameter count by approximately 41%, significantly enhancing computational efficiency. Compared to a previously proposed incremental architecture, the Proposed-II model achieves an 8.49% higher accuracy while reducing parameters by about 98.81% and MACs by about 98.97%, demonstrating exceptional resource efficiency. By utilizing XTTS and public corpora to generate a novel keyword speech dataset, this study enhances sample diversity and reduces reliance on large-scale original speech data. Experimental analysis reveals that an optimal synthetic-to-real speech ratio of 1:5 significantly improves the overall system accuracy, effectively addressing data scarcity. Additionally, the Proposed-I and Proposed-II models achieve accuracies of 97.54% and 98.66%, respectively, in distinguishing real from synthetic speech, demonstrating their strong potential for speech security and anti-spoofing applications. Full article

Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations. Full article

In the digital era, face recognition technology has emerged as a promising solution for enhancing payment system security and convenience. This systematic literature review examines face recognition advancements in payment security following the PRISMA framework. From 219 initially identified articles, we selected 10 studies meeting our technical criteria. The findings reveal significant progress in deep learning approaches, multimodal feature integration, and transformer-based architectures. Current trends emphasize multimodal systems combining RGB with IR and depth data for sophisticated attack detection. Critical challenges remain in cross-dataset generalization, evaluation standardization, computational efficiency, and combating advanced threats including deepfakes. This review identifies technical limitations and provides direction for developing robust facial recognition technologies for widespread payment adoption. Full article

Facial authentication has gained widespread adoption as a biometric authentication method, offering a convenient alternative to traditional password-based systems, particularly on mobile devices equipped with front-facing cameras. While this technology enhances usability and security by eliminating password management, it remains highly susceptible to spoofing attacks. Adversaries can exploit facial recognition systems using pre-recorded photos, videos, or even sophisticated 3D models of victims’ faces to bypass authentication mechanisms. The increasing availability of personal images on social media further amplifies this risk, making robust anti-spoofing mechanisms essential for secure facial authentication. To address these challenges, we introduce FaceCloseup, a novel liveness detection technique that strengthens facial authentication by leveraging perspective distortion inherent in close-up shots of real, 3D faces. Instead of relying on additional sensors or user-interactive gestures, FaceCloseup passively analyzes facial distortions in video frames captured by a mobile device’s camera, improving security without compromising user experience. FaceCloseup effectively distinguishes live faces from spoofed attacks by identifying perspective-based distortions across different facial regions. The system achieves a 99.48% accuracy in detecting common spoofing methods—including photo, video, and 3D model-based attacks—and demonstrates 98.44% accuracy in differentiating between individual users. By operating entirely on-device, FaceCloseup eliminates the need for cloud-based processing, reducing privacy concerns and potential latency in authentication. Its reliance on natural device movement ensures a seamless authentication experience while maintaining robust security. Full article

Face recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significant challenges and risks. Distinguishing between real and fake faces, i.e., face anti-spoofing (FAS), is crucial to the security of face recognition systems. With the advent of large-scale academic datasets in recent years, FAS based on deep learning has achieved a remarkable level of performance and now dominates the field. This paper systematically reviews the latest advancements in FAS based on deep learning. First, it provides an overview of the background, basic concepts, and types of FAS attacks. Then, it categorizes existing FAS methods from the perspectives of RGB (red, green and blue) modality and other modalities, discussing the main concepts, the types of attacks that can be detected, their advantages and disadvantages, and so on. Next, it introduces popular datasets used in FAS research and highlights their characteristics. Finally, it summarizes the current research challenges and future directions for FAS, such as its limited generalization for unknown attacks, the insufficient multi-modal research, the spatiotemporal efficiency of algorithms, and unified detection for presentation attacks and deepfakes. We aim to provide a comprehensive reference in this field and to inspire progress within the FAS community, guiding researchers toward promising directions for future work. Full article

We propose a deep learning-based face anti-spoofing method that utilizes both RGB and depth images for face recognition. The proposed method can detect spoofing attacks across various domain types using domain adversarial learning for preventing overfitting to a specific domain. A pre-trained face detection model and a face segmentation model are employed to detect a facial region from RGB images. The pixels outside the facial region in the corresponding depth image are replaced with the depth values of the nearest pixels in the facial region to minimize background influence. Subsequently, a network comprising convolutional layers and a self-attention layer extracts features from RGB and depth images separately, then fuses them to detect spoofing attacks. The proposed network is trained using domain adversarial learning to ensure robustness against various types of face spoofing attacks. The experiment results show that the proposed network reduces the average Attack Presentation Classification Error Rate (APCER) to 11.12% and 8.00% compared to ResNet and MobileNet, respectively. Full article

Currently, unsupervised anti-spoofing methods suffer from low accuracy and susceptibility to irrelevant factors. In order to solve these problems, this paper analyzes the contribution of different fingertip tissue structures to anti-spoofing tasks and proposes an unsupervised anti-spoofing method based on the weighted contribution of tissue structures. Unsupervised OCT anti-spoofing methods suffer from weak robustness and lack comprehensive exploration of sub-dermal structure features. The proposed method introduces quantified weights of fingertip tissue contributions with self-attention through the Shapley value. The module can amplify crucial fingertip features and extracts more key fingertip information, thereby improving the anti-spoofing performance. Full article

With the rapid advancement of unmanned system technologies, their applications in transportation, scientific research, economy, resource exploration, and military fields have become increasingly widespread. The navigation system, as a fundamental component of unmanned systems, plays a crucial role in ensuring their stability and reliability. However, as technology evolves, interference targeting Global Navigation Satellite Systems (GNSSs) has escalated, posing significant challenges in the research of unmanned systems. Navigation interference not only disrupts the normal operation of unmanned systems but also emerges as a pivotal element in counter-unmanned system strategies. This paper provides a comprehensive review of the classification of GNSS navigation interference and its potential impacts, thoroughly analyzing and comparing the strengths and weaknesses of various anti-GNSS interference technologies. Finally, the paper offers insights into the future development trends of anti-interference technologies for unmanned systems, aiming to provide valuable references for future research. Full article

Face anti-spoofing detection is crucial for identity verification and security monitoring. However, existing single-modal models struggle with feature extraction under complex lighting conditions and background variations. Moreover, the feature distributions of live and spoofed samples often overlap, resulting in suboptimal classification performance. To address these issues, we propose a jointly optimized framework integrating the Enhanced Channel Attention (ECA) mechanism and the Intra-Class Differentiator (ICD). The ECA module extracts features through deep convolution, while the Bottleneck Reconstruction Module (BRM) employs a channel compression–expansion mechanism to refine spatial feature selection. Furthermore, the channel attention mechanism enhances key channel representation. Meanwhile, the ICD mechanism enforces intra-class compactness and inter-class separability, optimizing feature distribution both within and across classes, thereby improving feature learning and generalization performance. Experimental results show that our framework achieves average classification error rates (ACERs) of 2.45%, 1.16%, 1.74%, and 2.17% on the CASIA-SURF, CASIA-SURF CeFA, CASIA-FASD, and OULU-NPU datasets, outperforming existing methods. Full article

With the advancement of technology, biometric recognition technology has gained widespread attention in identity authentication due to its high security and convenience. Finger vein recognition, as a biometric technology, utilizes near-infrared imaging to extract subcutaneous vein patterns, offering high security, stability, and anti-spoofing capabilities. Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. To address this issue, this paper proposes a high-accuracy and high-efficiency finger vein recognition model called Faster Multi-Scale Finger Vein Recognition Network (FMFVNet), which optimizes recognition speed through the FasterNet Block module while ensuring recognition accuracy with the Multi-Scale Convolutional Attention (MSCA) module. Experimental results show that on the FV-USM and SDUMLA-HMT datasets, FMFVNet achieves recognition accuracies of 99.80% and 99.06%, respectively. Furthermore, the model’s inference time is reduced to 1.75 ms, representing a 20.8% improvement over the fastest baseline model and a 62.7% improvement over the slowest, achieving more efficient finger vein recognition. Full article

Global Navigation Satellite System (GNSS) spoofing induces the target receiver to obtain the wrong positioning and timing results, which is very harmful. It is necessary to develop high-precision GNSS spoofing detection and associated direction-finding methods. In order to achieve sensitive and high-precision direction-finding for GNSS spoofing, it is necessary to realize the spoofing signal detection in the capture phase. This paper first proposes a method of GNSS spoofing detection, based on machine learning, that extracts features in the capture phase, which realizes various types of spoofing detection such as matching power, carrier phase alignment, and frequency locking. Notably, existing spoofing-direction-finding methods are mainly based on dedicated antenna arrays, which incur high costs and are not conducive to large-scale deployments. The basis of the spoofing detection proposed by this paper consists of a differential phase-center correction method, which is proposed in the context of an off-the-shelf anti-jamming array antenna, which effectively reduces the impact of the phase-center jitter introduced by the mutual coupling between antenna arrays on the direction-finding. The publicly accessible Texas Spoofing Test Battery (TEXBAT) dataset and actual measured data are both used for test verification. The results demonstrate that the proposed spoofing detection method can achieve success rates of over 97% on the TEXBAT dataset and more than 96% on the measured dataset, and the accuracy of the proposed direction-finding method can reach 1°, which can realize the effective detection and direction-finding of GNSS spoofing. Full article

In this paper, we address the challenge of fingerprint liveness detection by proposing a dual pre-trained model approach that combines VGG16 and ResNet50 architectures. While existing methods often rely on a single feature extraction model, they may struggle with generalization across diverse spoofing materials and sensor types. To overcome this limitation, our approach leverages the high-resolution feature extraction of VGG16 and the deep layer architecture of ResNet50 to capture a more comprehensive range of features for improved spoof detection. The proposed approach integrates these two models by concatenating their extracted features, which are then used to classify the captured fingerprint as live or spoofed. Evaluated on the Livedet2013 and Livedet2015 datasets, our method achieves state-of-the-art performance, with an accuracy of 99.72% on Livedet2013, surpassing existing methods like the Gram model (98.95%) and Pre-trained CNN (98.45%). On Livedet2015, our method achieves an average accuracy of 96.32%, outperforming several state-of-the-art models, including CNN (95.27%) and LivDet 2015 (95.39%). Error rate analysis reveals consistently low Bonafide Presentation Classification Error Rate (BPCER) scores with 0.28% on LivDet 2013 and 1.45% on LivDet 2015. Similarly, the Attack Presentation Classification Error Rate (APCER) remains low at 0.35% on LivDet 2013 and 3.68% on LivDet 2015. However, higher APCER values are observed for unknown spoof materials, particularly in the Crossmatch subset of Livedet2015, where the APCER rises to 8.12%. These findings highlight the robustness and adaptability of our simple dual-model framework while identifying areas for further optimization in handling unseen spoof materials. Full article

Face anti-spoofing (FAS) has always been a hidden danger in network security, especially with the widespread application of facial recognition systems. However, some current FAS methods are not effective at detecting different forgery types and are prone to overfitting, which means they cannot effectively process unseen spoof types. Different loss functions significantly impact the classification effect based on the same feature extraction without considering the quality of the feature extraction. Therefore, it is necessary to find a loss function or a combination of different loss functions for spoofing detection tasks. This paper mainly aims to compare the effects of different loss functions or loss function combinations. We selected the Swin Transformer as the backbone of our training model to extract facial features to ensure the accuracy of the ablation experiment. For the application of loss functions, we adopted four classical loss functions: cross-entropy loss (CE loss), semi-hard triplet loss, L1 loss and focal loss. Finally, this work proposed combinations of Swin Transformers and different loss functions (pairs) to test through in-dataset experiments with some common FAS datasets (CelebA-Spoofing, CASIA-MFSD, Replay attack and OULU-NPU). We conclude that using a single loss function cannot produce the best results for the FAS task, and the best accuracy is obtained when applying triplet loss, cross-entropy loss and Smooth L1 loss as a loss combination. Full article