An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

QuickSilver: modeling and parameterized verification for distributed agreement-based systems

The last decade has sparked several valiant efforts in deductive verification of distributed agreement protocols such as consensus and leader election. Oddly, there have been far fewer verification efforts that go beyond the core protocols and target applications that are built on top of agreement protocols. This is unfortunate, as agreement-based distributed services such as data stores, locks, and ledgers are ubiquitous and potentially permit modular, scalable verification approaches that mimic their modular design. We address this need for verification of distributed agreement-based systems through our novel modeling and verification framework, QuickSilver, that is not only modular, but also fully automated. The key enabling feature of QuickSilver is our encoding of abstractions of verified agreement protocols that facilitates modular, decidable, and scalable automated verification. We demonstrate the potential of QuickSilver by modeling and efficiently verifying a series of tricky case studies, adapted from real-world applications, such as a data store, a lock service, a surveillance system, a pathfinding algorithm for mobile robots, and more.

Probabilistic modelling and verification using RoboChart and PRISM

RoboChart is a timed domain-specific language for robotics, distinctive in its support for automated verification by model checking and theorem proving. Since uncertainty is an essential part of robotic systems, we present here an extension to RoboChart to model uncertainty using probabilism. The extension enriches RoboChart state machines with probability through a new construct: probabilistic junctions as the source of transitions with a probability value. RoboChart has an accompanying tool, called RoboTool, for modelling and verification of functional and real-time behaviour. We present here also an automatic technique, implemented in RoboTool, to transform a RoboChart model into a PRISM model for verification. We have extended the property language of RoboTool so that probabilistic properties expressed in temporal logic can be written using controlled natural language.

The Verification of Ecological Citizen Science Data: Current approaches and future possibilities

Citizen science schemes (projects) enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably among schemes. Here, we systematically review approaches to verification across ecological citizen science schemes, which feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used (Baker et al. 2021). We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes. Community consensus was the second most common verification approach, used by schemes such as Snapshot Serengeti (Swanson et al. 2016) and MammalWeb (Hsing et al. 2018). It was more common among schemes with a larger number of participants and where photos or video had to be submitted with each record. Automated verification was not widely used among the schemes reviewed. Schemes that used automation, such as eBird (Kelling et al. 2011) and Project FeederWatch (Bonter and Cooper 2012) did so in conjunction with other methods such as expert verification. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this hierachical system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.

Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.