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
Current identity-based （ID） cryptosystem lacks the mechanisms of two-party authentication and user＇s private key distribution. Some ID-based signcryption schemes and ID-based authenticated key agreement protocols h...Current identity-based （ID） cryptosystem lacks the mechanisms of two-party authentication and user＇s private key distribution. Some ID-based signcryption schemes and ID-based authenticated key agreement protocols have been presented, but they cannot solve the problem completely. A novel ID-based authentication scheme based on ID-based encrypfion （IBE） and fingerprint hashing method is proposed to solve the difficulties in the IBE scheme, which includes message receiver authenticating the sender, the trusted authority （TA） authenticating the users and transmitting the private key to them. Furthermore, the scheme extends the application of fingerprint authentication from terminal to network and protects against fingerprint data fabrication. The fingerprint authentication method consists of two factors. This method combines a token key, for example, the USB key, with the user＇s fingerprint hash by mixing a pseudo-random number with the fingerprint feature. The security and experimental efficiency meet the requirements of practical applications.展开更多
The cyber physical system is widely applied to industrial Internet of Things system and its security determines the safety of the industrial loT system.This paper proposes a multilevel fine fingerprint authentication ...The cyber physical system is widely applied to industrial Internet of Things system and its security determines the safety of the industrial loT system.This paper proposes a multilevel fine fingerprint authentication method for the security protection of key operating equipment.Firstly,Because of the acquired signals,the mathematical representation models are established separately,including the self-encoding model on the basis of the multiorder moment features and the multi-scale representation model based on the wavelet.Secondly,the multigranularity features of the time domain and frequency domain are extracted respectively based on the real-time data's self-encoding representation and multiscale representation.Then,the extracted features of different granularities can be built into the image representation based on the time-frequency fusion portrait.Based on the images of different granularities and the legitimate image library,a multiscale matching authentication method is established.Finally,simulation experiments show that the proposed method is effective.展开更多
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China Post-Doctor Science Fund (20060390414)the National Natural Science Foundation of China (90604022)+2 种基金the Natural Science Foundation of Beijing (4062025)the National Basic Research Program of China (2007CB311203)the 111 Project (B08004)
文摘Current identity-based （ID） cryptosystem lacks the mechanisms of two-party authentication and user＇s private key distribution. Some ID-based signcryption schemes and ID-based authenticated key agreement protocols have been presented, but they cannot solve the problem completely. A novel ID-based authentication scheme based on ID-based encrypfion （IBE） and fingerprint hashing method is proposed to solve the difficulties in the IBE scheme, which includes message receiver authenticating the sender, the trusted authority （TA） authenticating the users and transmitting the private key to them. Furthermore, the scheme extends the application of fingerprint authentication from terminal to network and protects against fingerprint data fabrication. The fingerprint authentication method consists of two factors. This method combines a token key, for example, the USB key, with the user＇s fingerprint hash by mixing a pseudo-random number with the fingerprint feature. The security and experimental efficiency meet the requirements of practical applications.
基金supported by the Natural Science Foundation of Huzhou(2023YZ47),the National Natural Science Foundation of China(62120106011,61933013,61733015 and U1934221)This work was supported by Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems(grant 2022-17)Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System.
文摘The cyber physical system is widely applied to industrial Internet of Things system and its security determines the safety of the industrial loT system.This paper proposes a multilevel fine fingerprint authentication method for the security protection of key operating equipment.Firstly,Because of the acquired signals,the mathematical representation models are established separately,including the self-encoding model on the basis of the multiorder moment features and the multi-scale representation model based on the wavelet.Secondly,the multigranularity features of the time domain and frequency domain are extracted respectively based on the real-time data's self-encoding representation and multiscale representation.Then,the extracted features of different granularities can be built into the image representation based on the time-frequency fusion portrait.Based on the images of different granularities and the legitimate image library,a multiscale matching authentication method is established.Finally,simulation experiments show that the proposed method is effective.
