As a typical eutectic high-entropy alloy(EHEA),AlCoCrFeNi2.1 exhibits excellent casting properties.However,the imbalance between strength and plasticity hinders its application as an advanced structural material.In or...As a typical eutectic high-entropy alloy(EHEA),AlCoCrFeNi2.1 exhibits excellent casting properties.However,the imbalance between strength and plasticity hinders its application as an advanced structural material.In order to address this challenge,deep cryogenic treatment(DCT)as a new process applied in the field of EHEAs was proposed in this study.The effects of different DCT times on the microstructure and mechanical properties of AlCoCrFeNi2.1 EHEAs were studied,mainly focusing on the flake structure of FCC+B2 layer.The experimental results suggest that with the extension of the DCT time,the dislocation density in the FCC phase increases significantly.The spherical BCC precipitate phase is generated within the B2 phase,and the average size of this newly generated precipitate phase gradually decreases.Increasing the number of dislocations and precipitate phases is of great significance to improve the mechanical properties.The AlCoCrFeNi2.1 EHEA exhibits excellent comprehensive mechanical properties after DCT for 36 h.Compared with the as-cast state,the tensile strength at room temperature reaches 1,034.51 MPa,increased by 5.74%.The plasticity reaches 21.72%,which is increased by 11.79%.The results show that the tensile strength and ductility of AlCoCrFeNi2.1 EHEAs are balanced and improved after DCT,which are more suitable as advanced structural materials.In addition,the introduction of the DCT process to EHEAs solves the problem of environmental pollution caused by traditional heat treatment process.This study provides useful guidance for using the DCT process to strengthen the mechanical properties of“lamellar+block”type EHEAs.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
基金financially supported by the National Natural Science Foundation of China(Nos.52301061,52204394)the Joint Fund Project of Science and Technology Plan of Liaoning Province(No.2023-MSLH-250)the Science and the Technology Program of Liaoning Provincial Department of Education(No.JYTQN2023286)。
文摘As a typical eutectic high-entropy alloy(EHEA),AlCoCrFeNi2.1 exhibits excellent casting properties.However,the imbalance between strength and plasticity hinders its application as an advanced structural material.In order to address this challenge,deep cryogenic treatment(DCT)as a new process applied in the field of EHEAs was proposed in this study.The effects of different DCT times on the microstructure and mechanical properties of AlCoCrFeNi2.1 EHEAs were studied,mainly focusing on the flake structure of FCC+B2 layer.The experimental results suggest that with the extension of the DCT time,the dislocation density in the FCC phase increases significantly.The spherical BCC precipitate phase is generated within the B2 phase,and the average size of this newly generated precipitate phase gradually decreases.Increasing the number of dislocations and precipitate phases is of great significance to improve the mechanical properties.The AlCoCrFeNi2.1 EHEA exhibits excellent comprehensive mechanical properties after DCT for 36 h.Compared with the as-cast state,the tensile strength at room temperature reaches 1,034.51 MPa,increased by 5.74%.The plasticity reaches 21.72%,which is increased by 11.79%.The results show that the tensile strength and ductility of AlCoCrFeNi2.1 EHEAs are balanced and improved after DCT,which are more suitable as advanced structural materials.In addition,the introduction of the DCT process to EHEAs solves the problem of environmental pollution caused by traditional heat treatment process.This study provides useful guidance for using the DCT process to strengthen the mechanical properties of“lamellar+block”type EHEAs.
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.
