The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating mult...The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.展开更多
Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is s...Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is still a challenging task. In this paper, we propose a real-time compressive tracking based on online Hough forest. The gray and texture features of discrete samples are extracted and compressed via the random measurement matrix. Online Hough forest classifier is used to vote the location probability of the target, and it optimizes the confidence map estimation for the target detection. The location of target being tracked is determined by combining the upper frame of the target center location and the probability confidence map of the incremental Hough forest. Finally, the classifier parameters are updated online by introducing the illumination variation and target occlusion feedback mechanism adaptively. The experiments with state-of-the-art algorithms on challenging sequences demonstrated that the proposed algorithm can effectively enhance the robustness and accuracy, and inherit the real-time performance of the compressive tracking algorithm.展开更多
This paper proposes a new secure e-voting protocol. This new scheme does not require a special voting channel and communication can occur entirely over the existing Internet. This method integrates Internet convenienc...This paper proposes a new secure e-voting protocol. This new scheme does not require a special voting channel and communication can occur entirely over the existing Internet. This method integrates Internet convenience and cryptology. In the existing protocols either the tallier has to wait for the decryption key from voter till the voting process is over or the verification process has to wait until the election is over. But in the proposed single transaction voting protocol the entire voting process as well as the verification process is done as a single transaction compared to multiple transactions in the existing protocol. The advantage of single transaction is that it consumes less time that results in overall speeding up the voting process. It is shown that the proposed scheme satisfies the more important requirements of any e-voting scheme: completeness, correctness, privacy, security and uniqueness. Finally, the proposed protocol is compared with the existing protocols such as Simple, Two Agency, Blind Signatures and sensus protocols.展开更多
基金funded by the Researchers Supporting Project Number(RSP2023R 102)King Saud University,Riyadh,Saudi Arabia.
文摘The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.
基金supported by National Natural Science Foundation of China(No.61203343)Natural Science Foundation of Hebei Province(No.E2014209106)+1 种基金Science and Technology Research Project of Hebei Provincial Department of Education(Nos.QN2016102 and QN2016105)the Graduate Student Innovation Fund of North China University of Science and Technology(No.2016S10)
文摘Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is still a challenging task. In this paper, we propose a real-time compressive tracking based on online Hough forest. The gray and texture features of discrete samples are extracted and compressed via the random measurement matrix. Online Hough forest classifier is used to vote the location probability of the target, and it optimizes the confidence map estimation for the target detection. The location of target being tracked is determined by combining the upper frame of the target center location and the probability confidence map of the incremental Hough forest. Finally, the classifier parameters are updated online by introducing the illumination variation and target occlusion feedback mechanism adaptively. The experiments with state-of-the-art algorithms on challenging sequences demonstrated that the proposed algorithm can effectively enhance the robustness and accuracy, and inherit the real-time performance of the compressive tracking algorithm.
文摘This paper proposes a new secure e-voting protocol. This new scheme does not require a special voting channel and communication can occur entirely over the existing Internet. This method integrates Internet convenience and cryptology. In the existing protocols either the tallier has to wait for the decryption key from voter till the voting process is over or the verification process has to wait until the election is over. But in the proposed single transaction voting protocol the entire voting process as well as the verification process is done as a single transaction compared to multiple transactions in the existing protocol. The advantage of single transaction is that it consumes less time that results in overall speeding up the voting process. It is shown that the proposed scheme satisfies the more important requirements of any e-voting scheme: completeness, correctness, privacy, security and uniqueness. Finally, the proposed protocol is compared with the existing protocols such as Simple, Two Agency, Blind Signatures and sensus protocols.
