Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern.A robust data security system should protect the information assets of any organization against cybercr...Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern.A robust data security system should protect the information assets of any organization against cybercriminal activities.The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence.But when it comes to security,it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security.Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard(AES)algorithm to construct their main security wall.In this paper,a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence.The new algorithm has been named Split-n-Swap(SnS).Performance analysis of the new modification algorithm has been performed using different measurement metrics.The experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization.A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.展开更多
Face verification systems are critical in a wide range of applications,such as security systems and biometric authentication.However,these systems are vulnerable to adversarial attacks,which can significantly compromi...Face verification systems are critical in a wide range of applications,such as security systems and biometric authentication.However,these systems are vulnerable to adversarial attacks,which can significantly compromise their accuracy and reliability.Adversarial attacks are designed to deceive the face verification system by adding subtle perturbations to the input images.These perturbations can be imperceptible to the human eye but can cause the systemtomisclassifyor fail torecognize thepersoninthe image.Toaddress this issue,weproposeanovel system called VeriFace that comprises two defense mechanisms,adversarial detection,and adversarial removal.The first mechanism,adversarial detection,is designed to identify whether an input image has been subjected to adversarial perturbations.The second mechanism,adversarial removal,is designed to remove these perturbations from the input image to ensure the face verification system can accurately recognize the person in the image.To evaluate the effectiveness of the VeriFace system,we conducted experiments on different types of adversarial attacks using the Labelled Faces in the Wild(LFW)dataset.Our results show that the VeriFace adversarial detector can accurately identify adversarial imageswith a high detection accuracy of 100%.Additionally,our proposedVeriFace adversarial removalmethod has a significantly lower attack success rate of 6.5%compared to state-of-the-art removalmethods.展开更多
文摘Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern.A robust data security system should protect the information assets of any organization against cybercriminal activities.The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence.But when it comes to security,it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security.Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard(AES)algorithm to construct their main security wall.In this paper,a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence.The new algorithm has been named Split-n-Swap(SnS).Performance analysis of the new modification algorithm has been performed using different measurement metrics.The experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization.A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.
基金funded by Institutional Fund Projects under Grant No.(IFPIP:329-611-1443)the technical and financial support provided by the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Face verification systems are critical in a wide range of applications,such as security systems and biometric authentication.However,these systems are vulnerable to adversarial attacks,which can significantly compromise their accuracy and reliability.Adversarial attacks are designed to deceive the face verification system by adding subtle perturbations to the input images.These perturbations can be imperceptible to the human eye but can cause the systemtomisclassifyor fail torecognize thepersoninthe image.Toaddress this issue,weproposeanovel system called VeriFace that comprises two defense mechanisms,adversarial detection,and adversarial removal.The first mechanism,adversarial detection,is designed to identify whether an input image has been subjected to adversarial perturbations.The second mechanism,adversarial removal,is designed to remove these perturbations from the input image to ensure the face verification system can accurately recognize the person in the image.To evaluate the effectiveness of the VeriFace system,we conducted experiments on different types of adversarial attacks using the Labelled Faces in the Wild(LFW)dataset.Our results show that the VeriFace adversarial detector can accurately identify adversarial imageswith a high detection accuracy of 100%.Additionally,our proposedVeriFace adversarial removalmethod has a significantly lower attack success rate of 6.5%compared to state-of-the-art removalmethods.
