Reviewerly/exHarmony huggingface dataset:
Image Forgery Detection via Forensic Similarity Graphs

In the article "Exposing Fake Images with Forensic Similarity Graphs", O. Mayer and M. C. Stamm introduce a novel image forgery detection method.The proposed method is built on a graph-based representation of images, where image patches are represented as the vertices of the graph, and the edge weights are assigned in order to reflect the forensic similarity between the connected patches.In this representation, forged regions form highly connected subgraphs.Therefore, forgery detection and localization can be cast as a cluster analysis problem on the similarity graph.The authors present two graph clustering methods to detect and localize image forgeries.In this paper, we present briefly the method and offer an online executable version allowing everyone to test it on their own suspicious images.
An Effective Approach for Image Cartoon and Texture Decomposition

Decomposition of an image into cartoon and texture components is desirable in many image processing and computer vision applications such as image segmentation, object detection, and image enhancement. In general, most of the available methods are suffering from a common drawback that is a loss of edge information while smoothing the texture or existence of some texture near the edges in the cartoon component after the smoothing. These shortcomings of the existing methods motivated us to design a method to efficiently smooth the texture component without largely affecting the edge information of a given image. The proposed method achieves this aim in two steps: The first step is the identification of the actual texture region, and the second step comprises of texture smoothening of identified texture region. The efficiency of the proposed method can be seen in the experimental section.
DFCNNet: A Convolutional Neural Network to Detect Deepfakes

As humanity cruises ahead in its artificial intelligence race to reach new horizons in the field, especially in the image processing arena, there have also emerged some “flies” in the ointment along the way. One such fly is the recent application known as deepfake. Deepfake signifies the media which is morphed using deep learning and artificial intelligence tools. These types of media cannot be distinguished easily and can fool one on the first instance. To counter deepfakes, deep learning is used in this paper. Here, convolutional neural networks (CNNs) are used to classify images from our dataset used (deepfake dataset). Deep learning networks to learn features of the deepfake images and the predict if an image is real or deepfake. CNNs are used as these have proved to be robust in the deep learning applications which deal with images.
Detection of Malicious URLs

In today’s world, the usage of the web is increasing for skilled and private use. Most of our activities are associated with the online, for example, surf-riding online for few data, sending or receiving mails, etc. Web malware attacks and its area units are susceptible to most of the vital sectors which ends in significant loss in terms of wealth and security. Hackers host uninvited content and attract naive users to become victims of scams, like stealing non-public data and malware installation. Malware attacks result in revenue losses in businesses. Malicious URLs play a major role in cyberattacks these days. Malicious URLs may be delivered to a person through messages, emails, or advertisements. It is imperative to act on such threats in a very timely manner. The traditional way to detect malicious URLs is by blacklisting which is done by the use of web databases. The most important disadvantage of blacklisting is that it fails to find the new URLs. The projected approach identifies Uniform Resource Locators (URL) by employing a machine learning approach of feature extraction, and its algorithmic rule called as long short-term memory (LSTM) and predicts whether it is attacked (malicious) URLs or benign URLs.
Dynamic Decentralized Group Signature Scheme for Privacy Protection in Blockchain

Group signature schemes can play a key role in privacy protection of blockchain-based applications because of its security properties like unforgeability, anonymity, unlinkability, traceability, etc. But the malicious group manager may collude with some group members leading to biased decisions. This issue was addressed by Devidas et al. [12] with a static decentralized group signature scheme(DGSS) by decentralizing the group manager. However, the limitation of DGSS is that it works only for static domains, where group members are fixed and will not allow new members to join and existing members to leave the network. In this paper, DGSS is extended to propose a dynamic decentralized group signature scheme which allows the group members to join and revoke at run-time. The performance of Devidas et al. [12] scheme is also improved by reducing the number of multiplications to make it suitable for user identity privacy protection in lightweight blockchains or memory constraint devices. The security analysis and proof of correctness for the proposed scheme are also discussed in this paper.
Expressing uncertainty in information systems analytics research: A demonstration of Bayesian analysis applied to binary classification problems

The measures typically used to assess binary classification problems fail to incorporate the uncertainty inherent to many contexts into the results. We propose using a Bayesian model to express the uncertainty in binary classification problems. This study identified 10 previous studies that provided sufficient data to demonstrate the use of Bayesian analysis in Information Systems (IS) contexts with varying levels of uncertainty. The analysis and user study show that the addition of Bayesian analysis is most useful in high uncertainty contexts with a wide interval for positive predictive value. Such an interval will lead to high uncertainty, even with very certain sensitivity and specificity. The usefulness of Bayesian analysis in conditions of medium uncertainty depends on the context. In conditions of low uncertainty, Bayesian analysis does not add much value. The user study showed that presenting models with uncertainty changed researcher perception of which model performed the best with 18 of 21 researchers changing their opinion. We recommend that authors estimate the uncertainty in their models and provide confusion matrices and prevalence estimates in their results to enable Bayesian analysis as research in a domain matures.
Recent Trends in Opinion Mining using Machine Learning Techniques

Opinion mining is a sub-field of data mining and natural language processing that concerns extracting users’ opinions and attitudes towards products or services from their comments on the web. Human beings rely heavily on their perceptions. When making a choice, other people’s perspectives are taken into account. Currently, billions of Internet users communicate their opinions on several disciplines via journals, discussion forums, and social media sites. Companies and institutions are constantly interested in hearing what the general public thinks regarding their services and goods. It is critical in e-commerce and e-tourism to dynamically evaluate the vast number of user data available on the Internet; as a result, it is essential to establish ways for analysing and classifying it. Opinion mining, also known as sentiment classification, autonomously extracts opinions, views, and feelings through literature, audio, and data inputs using natural language processing. This paper provides an understanding of the machine learning strategies for classifying comments and opinions. This paper compares various machine learning-based opinion mining techniques such as Naive Bayes, SVM, genetic algorithm, decision tree, etc.
Investigating Part-of-Speech Tagging in Khasi Using Naïve Bayes and Support Vector Machine

This paper presents the investigation of the Khasi language toward the PoS tagging systems. Khasi is an Austroasiatic language, which is spoken in Meghalaya, India. The foremost purpose of this paper is to develop part-of-speech (PoS) tagging for the Khasi language based on the support vector machine (SVM) and the Naïve Bayes (NB). This work is the first instance using SVM and NB approaches in Khasi for PoS tagging. Part-of-speech tagging performs a vital role in natural language processing (NLP). In this research work, we have used a PoS tagging corpus that is manually tagged by using the designed grammatical PoS classes. The annotation is done using 53 tags, and the corpus consists of around 75,000 tokens. The PoS tagging system is trained and tested with a ratio of 80:20. It is found that the system yielded promising outcomes while comparing the state-of-art results.
Digital geometry on a cubic stair-case mesh

• The cubic grid Z 3 plays important role in the (digital) world. • Meshes obtained by cutting Z 3 by oblique planes can display 2d images. • The dual semi-regular grid, the rhombille tiling is an oblique cubic mesh. • Shortest path algorithm and digital distance are computed on the rhombille tiling. • The rhombille tiling can be used as a digital canvas and for RGB pixel geometry. In this paper, we investigate digital geometry on the rhombille tiling, D(6,3,6,3), that is the dual of the semi-regular tiling called hexadeltille T(6,3,6,3) tiling and also known as trihexagonal tiling. In fact, this tiling can be seen as an oblique mesh of the cubic grid giving practical importance to this specific grid both in image processing and graphics. The properties of the coordinate systems used to address the tiles are playing crucial roles in the simplicity of various algorithms and mathematical formulae of digital geometry that allow to work on the grid in image processing, image analysis and computer graphics, thus we present a symmetric coordinate system. This coordinate system has a strong relation to topological/combinatorial coordinate system of the cubic grid. It is an interesting fact that greedy shortest path algorithm may not be used on this grid, despite to this, we present algorithm to provide a minimal-length path between each pair of tiles, where paths are defined as sequences of neighbor tiles (those are considered to be neighbors which share a side). We also prove closed formula for computing the digital, i.e., path-based distance, the length (the number of steps) of a/the shortest path(s). Some example pictures on this grid are also presented, as well as its possible application as pixel geometry for color images and videos on the hexagonal grid. To create your abstract, type over the instructions in the template box below. Fonts or abstract dimensions should not be changed or altered.
Profile-Based Calibration for AR/VR Glass

The purpose of this paper is to develop an innovative and affordable product, which will be so navigable for the day-to-day lives of the people. This paper ensures that people will be benefited a lot from their eye care to advanced technological features and also makes their days easier by displaying important day-to-day information as pop-ups and notifications in an interesting way. Android glasses which act as an independent device can be used to perform various tasks like an android smartphone. Voice command and a physical button can be used to interact with glass. Various tasks can be performed like navigation, making phone calls to setting reminders and alarms, and seeing weather information and news events. Augmented reality is used in various applications like maps and surroundings in which helps to understand the environment and reality. Even our glasses can automatically adjust the optical power according to the user’s profile. This ultimately helps to get a clear and high-definition image of our surroundings and objects we see on, helps on reading activity where user can also read small texts.
Potential Market-Predictive Features Based Bitcoin Price Prediction Using Machine Learning Algorithms

Bitcoin is a type of digital currency or computerized money that is utilised for speculation around the world. Bitcoins are files that are saved in a digital wallet programme on a mobile phone or a PC. Every transaction and its timestamp data are recorded in a common list known as blockchain. In this research, the cost of bitcoin is estimated utilising data mining techniques and machine learning algorithms. The dataset is preprocessed with the use of data mining algorithms, which reduces data noise. Bitcoin's price fluctuates, and it is estimated using long short-term memory (LSTM), a type of neural networking, to extract acceptable patterns for modelling and prediction. Discovering recurring patterns in the bitcoin market is a necessary endeavour in order to achieve optimal bitcoin price functionality. The dataset consists of numerous regularly reported bitcoin price features every year. Linear regression (LR) technique is used to estimate the future cost of bitcoin. Daily price shift with the best possible precision by using the available data is also estimated.
Acceleration of Computer Vision and Deep Learning

Cameras used for surveillance have grown in popularity since the technology boom and are now part of our everyday life. It appears to be laborious and time-consuming to monitor the surveillance cameras manually. Computer vision is reshaping the security and surveillance industry. Despite their revolutionary nature, modern CCTV cameras are insufficient because they are passive units that assist investigations but do not give preventative measures. They need to be replaced with active systems. Recreational issues in public security necessitate computer vision, artificial intelligence, and machine learning (ML). Due to the advancements in deep surveillance, we should expect a massive boost in inefficiency. The outstanding performance in image identification and the capacity to absorb temporal information that convolutional and recurrent neural networks offer intelligent surveillance systems bodes well for their future growth in this area. The goal of this chapter is to examine the challenges in surveillance systems, the usage of deep learning techniques, and various new applications.
Investigation of Trust Models to Alleviate the Authentication Challenge in FinTech

FinTech applications are increasingly vulnerable to cyber-attacks (identity theft, data breaches, distributed denial of service attacks, phishing attacks, insider threats), which have become a threat to many firms. These threats against FinTech services have the potential to wreak enormous societal, economic, and organizational harm. It is noted that various proposed methods failed to address the fundamental FinTech security issues of scalability, privacy, and trust distribution. Various well-known compliances of all FinTech are being adopted by developed countries to counter these cyber-attacks. Blockchain arouses increasing interest in different economic sectors, with confidentiality, availability, and integrity being fundamental factors. The study shows that the mass adoption of blockchain-based trust models has accelerated in the financial industry with private permissioned blockchain. The primary goal of these trust models is to assure the security, reliability, trustworthiness, and implementation of FinTech compliance.
Impact of Deepfake Technology on FinTech Applications

The distribution of fabricated disinformation through deliberate manipulation of audio/video content by imposters with the intent to affect organization is deepfake. The “infodemic” that spread alongside the COVID-19 pandemic also increased cyber risk in financial technology (FinTech) applications. The continuous evolution of cybercrime has culminated with deepfakes which severely magnify the threats of traditional frauds. Recent evidence indicates that deepfake videos are mainly created with the help of artificial intelligence (AI) or machine learning (ML) techniques. This results in creation of fake videos by merging, superimposing, and replacing actual video clips and images with other videos. There are a lot of people who accept deepfake videos as actual videos without any doubt. The use of AL and ML techniques have made video/image forgery difficult to identify with the help of existing deepfake detection techniques. Deepfake technology is becoming more and more sophisticated, and detection of fake videos is relatively challenged for quite some time.
Perceived Cyber Security Challenges in Adoption and Diffusion of FinTech Services in India

FinTech is a term that refers to a new type of digital technology that intends to build up and automate the distribution and management of financial services. FinTech is an abbreviation for “financial technology.” FinTech, or financial technology, assists companies, business holders, and consumers in managing their financial procedures and methods. The high adoption rate of fintech services creates a whole ecosystem of looters and hackers. This indeed is scary, and this chapter makes an attempt to understand the adoption rate of fintech services and diffusion challenges at the same time.
The Evolution of Image Denoising From Model-Driven to Machine Learning

Image denoising is a class of image processing algorithms that aim to enhance the visual quality of the acquired images by removing noise inherent in them and is an active area of research under image enhancement and reconstruction techniques. Traditional model-driven methods are motivated by statistical assumptions on data corruption and prior knowledge of the data to recover while the machine learning (ML) approaches require a massive amount of training data. However, the manual tuning of hyperparameters in model-driven approaches and susceptibility to overfitting under learning-based techniques are their major flaws. Recent years have witnessed the amalgamation of both model and ML-based approaches. Infusing model-driven Bayesian estimator in an ML-based approach, supported by robust mathematical arguments, has been shown to achieve optimal denoising solutions in real time with less effect of over-fitting. In this chapter, the evolution of image denoising techniques is covered from a mathematical perspective along with detailed experimental analysis for each class of approach.
Quantum and Post-Quantum Cybersecurity Challenges and Finance Organizations Readiness

Cryptography is used to protect sensitive information, but it is also required in many applications to ensure secure functionality and availability. The 100-year-old principles of physics are becoming industrially controllable, which leads to the era of the industrial quantum revolution. Products and applications such as quantum sensors, quantum simulators, quantum computers, and quantum cryptography are developing, which will affect the design of secure cryptographic systems. Post-quantum cryptography is a new field of research developing parallel to the progress in quantum technologies. Post-quantum cryptography deals with the development and investigation of algorithms that are assumed to be unbreakable even with quantum computers. This chapter will discuss the quantum and post-quantum cryptographic algorithms in detail and the migration strategies from classical asymmetric algorithms to post-quantum algorithms. This chapter also discusses the finance organization's readiness and recommendation for the replacement of vulnerable asymmetric algorithms with post-quantum algorithms.
The Insider Threat Landscape and the FinTech Sector

The increased reliance on online services during the recent pandemic has compelled businesses and operations to make a major technological shift to ensure online presence. However, this online paradigm shift has also attracted malicious agents who aim to benefit from vulnerabilities in the cyber world. These attackers target a wide spectrum of sectors including healthcare, government agencies, education, and financial services. In this chapter, the impact of insider security attacks on FinTech applications is discussed. A detailed account of insider attacks applicable on FinTech applications is provided. Moreover, solutions and recommendations are provided to make FinTech applications more secure by preventing and defending against insider attacks benefitting from emerging fields like crypto-currency, micro-finance, and robo-advisors. Successful prevention and defense against these attacks will ensure that the FinTech industry can be secure and can contribute in, as laid out in UN's Sustainable Development Goals, decent work and economic growth for the masses.
Gesture and Posture Recognition by Using Deep Learning

Sign language facilitates communication in the community with speaking and hearing problems. Those people communicate with one another using hand gestures and body movements. These techniques of human-computer interaction range from primary keyboard inputs to complex vision-based gesture detection systems. One of the fascinating HCI technologies is hand gesture recognition. The goal of gesture recognition is to construct a system to use as a communication medium in various applications. The application of gesture recognition has become popular in healthcare, robotics, etc. Posture recognition is deployed in several sectors, including medicine. Deep learning-based models are used to understand gesture and posture recognition results better. This chapter covers the challenges of gesture and posture recognition and how deep learning techniques are used to assist machines in overcoming these challenges more effectively. In addition, this chapter also discusses the applications in which gesture and posture recognition can be employed in detail.
Role of Deep Learning in Image and Video Processing

Image and video processing research is becoming an important area in the field of computer vision. There are challenges such as low-resolution images, poor quality of videos, etc. in image and video data processing. Deep learning is a machine learning technique used in the creation of AI systems. It is designed to analyse complex data by passing it through many layers of neurons. Deep learning techniques have the potential to produce cutting-edge results in difficult computer vision problems such as object identification and face recognition. In this chapter, the use of deep learning to target specific functionality in the field of computer vision such as image recovery, video classification, etc. The deep learning algorithms, such as convolutional neural networks, deep neural network, and recurrent neural networks, used in the image and video processing domain are also explored.
An Intelligent 8-Queen Placement Approach of Chess Game for Hiding 56-Bit Key of DES Algorithm Over Digital Color Images

Secured data transmission between the communication channels would be a challenging. Attainment of secured key transmission of cryptographic algorithms between communication channel partners is one of the most difficult tasks in data communication. Various cryptography and steganographic principles have been presented for this purpose. This chapter presents a new steganographic approach in which the 56-bit key of data encryption standard (DES) algorithm is safely conveyed between communicators by embedding it in a color digital image. The popular chess game-based 8 Queens placement scheme is used to identify pixel positions for the key embedding process. From observational consequences, it is accomplished that the nominated scheme would lead to achieving assured content contagion over the network.