Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private s...Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors.Therefore,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be investigated.This work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and fingerprints.The proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy.The employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric features.These obtained features are utilized to generate two random chaos matrices.The first random chaos matrix is used to permute the pixels of biometric images.In contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)algorithm.To assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were measured.The assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing BSSmodels.For example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.展开更多
In response to the current gaps in ef-fective proactive defense methods within applica-tion security and the limited integration of security components with applications,this paper proposes a biomimetic security model...In response to the current gaps in ef-fective proactive defense methods within applica-tion security and the limited integration of security components with applications,this paper proposes a biomimetic security model,called NeuroShield,specifically designed for web applications.Inspired by the“perception-strategy-effect-feedback”mechanism of the human nervous control system,the model inte-grates biomimetic elements akin of neural receptors and effectors into applications.This integration fa-cilitates a multifaceted approach to security:enabling data introspection for detailed perception and regula-tion of application behavior,providing proactive de-fense capabilities to detect and block security risks in real-time,and incorporating feedback optimization to continuously adjust and enhance security strategies based on prevailing conditions.Experimental results affirm the efficacy of this neural control mechanism-based biomimetic security model,demonstrating a proactive defense success rate exceeding 95%,thereby offering a theoretical and structural foundation for biomimetic immunity in web applications.展开更多
Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorp...Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.展开更多
The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advant...The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.展开更多
With the advancement in internet technologies, the number of servers has increased remarkably to provide more services to the end users. These services are provided over the public channels, which are insecure and sus...With the advancement in internet technologies, the number of servers has increased remarkably to provide more services to the end users. These services are provided over the public channels, which are insecure and susceptible to interception, modification, and deletion. To provide security, registered entities are authenticated and then a session key is established between them to communicate securely. The conventional schemes anow a user to access services only after their independent registration with each desired server in a multiserver system. Therefore, a user must possess multiple smartcards and memorize various identities and passwords for obtaining services from multiple servers. This has led to the adoption of multiserver authentication in which a user accesses services of multiple servers after registering himself at only one central authority. Recently, Kumar and Om discussed a scheme for multiserver environment by using smartcard. Since the user-memorized passwords are of low entropy, it is possible for an attacker to guess them. This paper uses biometric information of user to enhance the security of the scheme by Kumar and Ore. Moreover, we conducted rigorous security analyses （informal and formal） in this study to prove the security of the proposed scheme against all known attacks. We also simulated our scheme by using the automated tool, ProVerif, to prove its secrecy and authentication properties. A comparative study of the proposed scheme with the existing related schemes shows its effectiveness.展开更多
Human gait is one of the unobtrusive behavioral biometrics that has been extensively studied for various commercial and government applications.Biometric security,medical rehabilitation,virtual reality,and autonomous ...Human gait is one of the unobtrusive behavioral biometrics that has been extensively studied for various commercial and government applications.Biometric security,medical rehabilitation,virtual reality,and autonomous driving cars are some of the fields of study that rely on accurate gait recognition.While majority of studies have been focused on achieving very high recognition performance on a specific dataset,different issues arise in the real-world applications of this technology.This research is one of the first to evaluate the effects of changing walking speeds and directions on gait recognition rates under various walking conditions.Dataset was collected using the KINECT sensor.To draw an overall conclusion about the effects of walking speed and di-rection to the sensor,we define distance features and angle features.Furthermore,we propose two feature fusion methods for person recognition.Results of the study provide insights into how walking speeds and walking di-rections to the KINECT sensor influence the accuracy of gait recognition.展开更多
文摘Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors.Therefore,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be investigated.This work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and fingerprints.The proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy.The employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric features.These obtained features are utilized to generate two random chaos matrices.The first random chaos matrix is used to permute the pixels of biometric images.In contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)algorithm.To assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were measured.The assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing BSSmodels.For example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.
基金The Fundamental Research Funds for the Central Universities(No.2242022k60005)Purple Mountain Laboratories for Network and Communication Security,and National Science Foundation(No.62233003).
文摘In response to the current gaps in ef-fective proactive defense methods within applica-tion security and the limited integration of security components with applications,this paper proposes a biomimetic security model,called NeuroShield,specifically designed for web applications.Inspired by the“perception-strategy-effect-feedback”mechanism of the human nervous control system,the model inte-grates biomimetic elements akin of neural receptors and effectors into applications.This integration fa-cilitates a multifaceted approach to security:enabling data introspection for detailed perception and regula-tion of application behavior,providing proactive de-fense capabilities to detect and block security risks in real-time,and incorporating feedback optimization to continuously adjust and enhance security strategies based on prevailing conditions.Experimental results affirm the efficacy of this neural control mechanism-based biomimetic security model,demonstrating a proactive defense success rate exceeding 95%,thereby offering a theoretical and structural foundation for biomimetic immunity in web applications.
文摘Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.
基金The research is supported by Nature Science Foundation of Zhejiang Province(LQ20F020008)“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant Nos.2023C03203,2023C01150).
文摘The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.
文摘With the advancement in internet technologies, the number of servers has increased remarkably to provide more services to the end users. These services are provided over the public channels, which are insecure and susceptible to interception, modification, and deletion. To provide security, registered entities are authenticated and then a session key is established between them to communicate securely. The conventional schemes anow a user to access services only after their independent registration with each desired server in a multiserver system. Therefore, a user must possess multiple smartcards and memorize various identities and passwords for obtaining services from multiple servers. This has led to the adoption of multiserver authentication in which a user accesses services of multiple servers after registering himself at only one central authority. Recently, Kumar and Om discussed a scheme for multiserver environment by using smartcard. Since the user-memorized passwords are of low entropy, it is possible for an attacker to guess them. This paper uses biometric information of user to enhance the security of the scheme by Kumar and Ore. Moreover, we conducted rigorous security analyses （informal and formal） in this study to prove the security of the proposed scheme against all known attacks. We also simulated our scheme by using the automated tool, ProVerif, to prove its secrecy and authentication properties. A comparative study of the proposed scheme with the existing related schemes shows its effectiveness.
文摘Human gait is one of the unobtrusive behavioral biometrics that has been extensively studied for various commercial and government applications.Biometric security,medical rehabilitation,virtual reality,and autonomous driving cars are some of the fields of study that rely on accurate gait recognition.While majority of studies have been focused on achieving very high recognition performance on a specific dataset,different issues arise in the real-world applications of this technology.This research is one of the first to evaluate the effects of changing walking speeds and directions on gait recognition rates under various walking conditions.Dataset was collected using the KINECT sensor.To draw an overall conclusion about the effects of walking speed and di-rection to the sensor,we define distance features and angle features.Furthermore,we propose two feature fusion methods for person recognition.Results of the study provide insights into how walking speeds and walking di-rections to the KINECT sensor influence the accuracy of gait recognition.
