OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

A genome scan of recent positive selection signatures in three sheep populations

Domesticated sheep have been exposed to artificial selection for the production of fiber, meat, and milk as well as to natural selection. Such selections are likely to have imposed distinctive selection signatures on the sheep genome. Therefore, detecting selection signatures across the genome may help elucidate mechanisms of selection and pinpoint candidate genes of interest for further investigation. Here, detection of selection signatures was conducted in three sheep breeds, Sunite （n=66）, German Mutton （n=159）, and Dorper （n=93）, using the Illumina OvineSNP50 Genotyping BeadChip array. Each animal provided genotype information for 43 273 autosomal single nucleotide polymorphisms （SNPs）. We adopted two complementary haplotype-based statistics of relative extended haplotype homozygosity （REHH） and the cross-popu- lation extended haplotype homozygosity （XP-EHH） tests. In total, 707,755, and 438 genomic regions subjected to positive selection were identified in Sunite, German Mutton, and Dorper sheep, respectively, and 42 of these regions were detected using both REHH and XP-EHH analyses. These genomic regions harbored many important genes, which were enriched in gene ontology terms involved in muscle development, growth, and fat metabolism. Fourteen of these genomic regions overlapped with those identified in our previous genome-wide association studies, further indicating that these genes under positive selection may underlie growth developmental traits. These findings contribute to the identification of candidate genes of interest and aid in understanding the evolutionary and biological mechanisms for controlling complex traits in Chinese and western sheep.

A Practical Comparison between Signature Approach and Other Existing Approaches in Error Detection over TCP

Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time.

Functional Verification of Signature Detection Architectures for High Speed Network Applications

To meet the future internet traffic challenges, enhancement of hardware architectures related to network security has vital role where software security algorithms are incompatible with high speed in terms of Giga bits per second (Gbps). In this paper, we discuss signature detection technique (SDT) used in network intrusion detection system (NIDS). Design of most commonly used hardware based techniques for signature detection such as finite automata, discrete comparators, Knuth-Morris-Pratt (KMP) algorithm, content addressable memory (CAM) and Bloom filter are discussed. Two novel architectures, XOR based pre computation CAM (XPCAM) and multi stage look up technique (MSLT) Bloom filter architectures are proposed and implemented in third party field programmable gate array (FPGA), and area and power consumptions are compared. 10Gbps network traffic generator (TNTG) is used to test the functionality and ensure the reliability of the proposed architectures. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of performance, speed and powerefficiency.

Structured sparsity assisted online convolution sparse coding and its application on weak signature detection

Due to the strong background noise and the acquisition system noise,the useful characteristics are often difficult to be detected.To solve this problem,sparse coding captures a concise representation of the high-level features in the signal using the underlying structure of the signal.Recently,an Online Convolutional Sparse Coding(OCSC)denoising algorithm has been proposed.However,it does not consider the structural characteristics of the signal,the sparsity of each iteration is not enough.Therefore,a threshold shrinkage algorithm considering neighborhood sparsity is proposed,and a training strategy from loose to tight is developed to further improve the denoising performance of the algorithm,called Variable Threshold Neighborhood Online Convolution Sparse Coding(VTNOCSC).By embedding the structural sparse threshold shrinkage operator into the process of solving the sparse coefficient and gradually approaching the optimal noise separation point in the training,the signal denoising performance of the algorithm is greatly improved.VTNOCSC is used to process the actual bearing fault signal,the noise interference is successfully reduced and the interest features are more evident.Compared with other existing methods,VTNOCSC has better denoising performance.

Intrinsic component filtering for fault diagnosis of rotating machinery

Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.