Graphical Password Research Articles

Analysis and Design of Efficient Authentication Techniques for Password Entry with the Qwerty Keyboard for VR Environments.

Authentication in digital security relies heavily on text-based passwords, even with other available methods like biometrics and graphical passwords. While virtual reality (VR) keyboards are typically invisible to onlookers, the presence of inconspicuous sensors, including accelerometers, gyroscopes, and barometers, poses a potential risk of unauthorized observation and recording. Traditional defense shoulder-surfing attack methods typically involve breaking apart the Qwerty layout, which destroys the user's inherent familiarity with the layout. This research addresses the need for secure password entry in VR environments while retaining the Qwerty layout. We explore three keyboard-related position alteration strategies to ensure security while mitigating the decline in user experience. These strategies involve moving the entire keyboard, cursor, and keys. Our theoretical study assesses the effectiveness of these strategies against shoulder-surfing attacks. Two user studies, employing ray-based and position-based text entry methods, respectively, evaluate the practical effectiveness of the three strategies in resisting shoulder-surfing attacks, as well as their impact on typing performance and user experience. Our findings demonstrate that the three strategies achieve shoulder-surfing attack resistance comparable to a random layout keyboard. Moreover, compared to a random layout, the two strategies involving the movement of the entire keyboard and the repositioning of keys support faster entry rates and enhanced user experience.

Visual Lock - Enhancing Security with Visual Password

The prevailing method of user authentication commonly involves the submission of a text-based password along with a username. However, this approach has inherent drawbacks that have been widely acknowledged. A significant challenge is the difficulty users face in remembering passwords, leading to the adoption of short and simplistic ones, which in turn renders them susceptible to easy guessing or hacking. To address these concerns, this article introduces a novel financial graphical password authentication system. The operational details are elucidated through real-world examples, emphasizing key features of the system. In response to the limitations of conventional text-based password authentication, a groundbreaking financial graphical password method is proposed in this article. The prevailing issue of users resorting to short and easily guessable passwords due to memory constraints is addressed through this innovative approach. The article not only explains the functioning of the new system using real-world instances but also underscores its crucial features that enhance security in user authentication

Advancements and Applications of Anti-Fraud Graphical Passwords

Visual cryptography represents a sophisticated and innovative approach to secure information sharing, gaining traction within the field of information security. This technology adeptly transforms sensitive data into visually interpretable graphics or images. These images are then segmented into multiple independent parts, each distributed among different stakeholders. This strategy ensures that no single party can access the complete data without collaboration, thereby enhancing security. This dissertation delves into several anti-fraud visual password schemes designed to mitigate risks associated with deceptive behaviors by participants and distributors alike. By addressing these vulnerabilities, the proposed schemes enhance the integrity and reliability of the data sharing process. The application of visual passwords in real-life scenarios not only bolsters data security but also shields private information from potential leaks. By reducing the complexity and cost associated with traditional decryption methods, visual cryptography offers substantial support for a wide array of application scenarios. This dissertation presents a comprehensive exploration of visual cryptography's potential to revolutionize data protection strategies, illustrating its importance and utility in contemporary digital environments.

Pivot: Panoramic-image-based VR User Authentication against Side-Channel Attacks

With metaverse attracting increasing attention from both academic and industry, the application of virtual reality (VR) has extended beyond 3D immersive viewing/gaming to a broader range of areas, such as banking, shopping, tourism, education, etc., which involves a growing amount of sensitive and private user data into VR systems. However, with current password-based user authentication schemes in mainstream VR devices, studies demonstrate that side-channel attacks can pose a severe threat to VR user privacy. To mitigate the threat, we propose a novel Panoramic-image-based VR user authentication system, i.e., Pivot , to defend against such attacks, yet maintain high usability. Specifically, in Pivot , we design an image-random-pivoting-based user interaction mechanism to assist users in quickly and securely selecting memorable points of interest in a panoramic image. Then an image region segmentation algorithm is designed to automatically scatter the points to regions to form the customized graphic password for the user, which could ensure a sufficiently large password space and also reduce the near-region point misclicks. Afterward, the region indexes are used to generate the hashed password for authentication. Both theoretical security analysis and extensive user studies demonstrate that Pivot is secure and user-friendly in practice.

Improving the Usability of Graphical Authentication Systems Using Subject-Based Images

Recognition based graphical authentication systems rely on a system user’s ability to recognize previously chosen images and use those images to gain access into a computing systems, devices or application. Recognition based systems that utilise several types of images have been developed and studied. The déjà vu scheme depends on the use of abstract (meaningless) images. Images in the déjà vu scheme have no exact meaning. This scheme has also been actively studied. This article presents a between user study conducted on two recognition-based schemes using system prototypes. The Abstract Based Graphical Authentication (ABGA) was designed to simulate the déjà vu scheme. The performance of this system was compared with the performance of a novel system developed for this research. The novel system, called Subject Based Graphical Authentication (SBGA), in which the system used images from the various disciplines of the research participants. The researcher conducted a lab-based study with 100 participants, 50 participants for each of the prototypes. The study investigated the impact of using subject based images to enhance the usability and security of recognition-based graphical passwords. Two graphical models were thus developed and presented to the 100 participants, which comprised of staff and students from the departments of Computer Science, Mathematics, and Engineering. In the experiments, participants selected their graphical passwords from sets of images representing their various disciplines. The researcher observed that the memorability rate for graphical passwords consisting of subject based images aligned with participants' disciplines surpassed the rate for passwords with purely abstract images. The results indicated 81.7% successful login rate and 18.3% failure rate for Subject Based Graphical Authentication (SBGA). In contrast, Abstract Based Graphical Authentication (ABGP) exhibited a 51.5% successful login rate and a 48.7% failure rate. The findings provide significant insights for graphical password designers and developers seeking to enhance system memorability. Balancing image familiarity with security considerations can lead to more user-friendly and effective recognition-based authentication systems. This study provides valuable insights into the benefits of incorporating familiar images in recognition-based graphical passwords, showcasing a significant improvement in memorability and user authentication success. The findings underscore the importance of considering the user’s familiarity with portfolio images in the design of graphical authentication systems.

New Test to Detect Clustered Graphical Passwords in Passpoints Based on the Perimeter of the Convex Hull

This research paper presents a new test based on a novel approach for identifying clustered graphical passwords within the Passpoints scenario. Clustered graphical passwords are considered a weakness of graphical authentication systems, introduced by users during the registration phase, and thus it is necessary to have methods for the detection and prevention of such weaknesses. Graphical authentication methods serve as a viable alternative to the conventional alphanumeric password-based authentication method, which is susceptible to known weaknesses arising from user-generated passwords of this nature. The test proposed in this study is based on estimating the distributions of the perimeter of the convex hull, based on the hypothesis that the perimeter of the convex hull of a set of five clustered points is smaller than the one formed by random points. This convex hull is computed based on the points that users select as passwords within an image measuring 1920 × 1080 pixels, using the built-in function convhull in Matlab R2018a relying on the Qhull algorithm. The test was formulated by choosing the optimal distribution that fits the data from a total of 54 distributions, evaluated using the Kolmogorov–Smirnov, Anderson–Darling, and Chi-squared tests, thus achieving the highest reliability. Evaluating the effectiveness of the proposed test involves estimating type I and II errors, for five levels of significance α∈{0.01,0.02,0.05,0.1,0.2}, by simulating datasets of random and clustered graphical passwords with different levels of clustering. In this study, we compare the effectiveness and efficiency of the proposed test with existing tests from the literature that can detect this type of pattern in Passpoints graphical passwords. Our findings indicate that the new test demonstrates a significant improvement in effectiveness compared to previously published tests. Furthermore, the joint application of the two tests also shows improvement. Depending on the significance level determined by the user or system, the enhancement results in a higher detection rate of clustered passwords, ranging from 0.1% to 8% compared to the most effective previous methods. This improvement leads to a decrease in the estimated probability of committing a type II error. In terms of efficiency, the proposed test outperforms several previous tests; however, it falls short of being the most efficient, using computation time measured in seconds as a metric. It can be concluded that the newly developed test demonstrates the highest effectiveness and the second-highest efficiency level compared to the other tests available in the existing literature for the same purpose. The test was designed to be implemented in graphical authentication systems to prevent users from selecting weak graphical passwords, enhance password strength, and improve system security.

Beyond passwords: A multi‐factor authentication approach for robust digital security

AbstractMulti‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.

Multi-Factor Authentication: A User Experience Study

Usability issues prevent multi-factor authentication (MFA) systems from being widely used, despite the fact that they are essential for improving security measures. This study explores the problems with MFA systems' usability and suggests possible fixes to enhance the user experience. The study emphasizes the value of good design while highlighting the efficacy of graphical password systems as a user-friendly substitute for conventional MFA techniques. The report also makes recommendations for future research directions, such as assessing resilience against complex assaults, improving support for mobile applications, and weighing the benefits and drawbacks of image-based authentication systems. Comprehensive user research and assessments can yield important insights to improve the effectiveness and usability of different MFA approaches. Additionally, a two-way image-based mobile device authentication system devices is introduced in the article, demonstrating improved login success rates and security threat resistance. In order to further enhance the authentication process, future development will focus on scalability, user input surveys, and bolstering system security. Keywords: Multi-Factor Authentication (MFA), User Experience (UX), Security, Usability, Graphical Passwords, Biometric Authentication, Mobile Devices, Image- Based Authentication, Psychological Aspects, Resilience,Scalability, Advanced Security Features

Natural language processing and stable diffusion model based graphical authentication using passphrase

Graphical passwords or passphrases (GPs) are examined as reliable authentication system over text-based passphrases. Several obtainable applications utilize GPs and image-based authentication schemes. Even though, these authentication methods experience an issue of managing image or pictorial data. Here, Natural Language Processing (NLP)+Stable Diffusion (SD)_graphical authentication is presented for graphical authentication using passphrase. The passphrase, removing stop words, spell check, similarity check, stemming and lemmatization, label encoder, positional embedding and encryption are the steps carried out in registration phase whereas decryption Advanced Encryption Standard (AES), sentence generation, image set generation and grid module are conducted in verification phase. Initially, passphrase is considered and then, stop words are removed from it. Thereafter, spell check as well as similarity check are done and after these processes stemming and lemmatization is performed. In label encoder, label is generated for individual word in passphrase. Afterwards, positional embedding is done and lastly, encryption is conducted in registration phase. In verification phase, encrypted passphrase is decrypted, sentence is generated utilizing Generative Pre-trained Transformer (GPT), image set is generated by SD model and finally, image grid module is accomplished. Moreover, NLP+SD_graphical authentication achieved minimum attack detection, login failure, login time about 0.598, 0.191, 14.175sec and maximum memorability about 0.932.

A Survey on Graphical Password Strategy Authentication

In an ever-evolving digital realm, ensuring the security and reliability of software applications has become increasingly crucial. The continuous advancement of technology also brings about more complex cyber threats, emphasizing the urgent need to identify and address vulnerabilities. In response to this growing imperative, we introduce "Graphical Password Authenticator: A Novel Approach to Secure User Authentication." The Graphical Password Authenticator reimagines the conventional user authentication process by utilizing graphical elements and innovative techniques. It provides a secure and user-friendly alternative to traditional text-based passwords, offering enhanced protection against unauthorized access. By incorporating graphical images, patterns, or other visual cues, users can create unique and memorable authentication credentials that are difficult for malicious actors to compromise. This approach offers a more intuitive and engaging way for users to access their accounts while significantly reducing the risk of password-related security breaches. The Graphical Password Authenticator system ensures robust security through a combination of graphical recognition and encryption technologies. It is designed to be resistant to common attack methods such as brute force and keyloggers, providing a higher level of protection for user accounts and sensitive information. This novel approach to user authentication fosters a secure and user-centric environment, making it a promising solution for the ever-evolving digital landscape. The Graphical Password Authenticator is a groundbreaking step at the intersection of user authentication and innovation. By redefining the way users prove their identity through memorable graphical elements, this approach empowers users with a more secure and intuitive means of accessing their digital resources. This paradigm shift in authentication methods showcases the potential of technology to enhance user security and create a safer digital future, ultimately bridging the gap between user convenience and robust cybersecurity. Keywords: Graphical Password Authenticator, User Authentication, Cybersecurity, Digital Security, Encryption Technologies, User-Centric Approach, Brute Force Resistance, Keyloggers Protection.

A Research on Graphical Password Strategy Authentication

In an ever-evolving digital realm, ensuring the security and reliability of software applications has become increasingly crucial. The continuous advancement of technology also brings about more complex cyber threats, emphasizing the urgent need to identify and address vulnerabilities. In response to this growing imperative, we introduce "Graphical Password Authenticator: A Novel Approach to Secure User Authentication." The Graphical Password Authenticator reimagines the conventional user authentication process by utilizing graphical elements and innovative techniques. It provides a secure and user-friendly alternative to traditional text-based passwords, offering enhanced protection against unauthorized access. By incorporating graphical images, patterns, or other visual cues, users can create unique and memorable authentication credentials that are difficult for malicious actors to compromise. This approach offers a more intuitive and engaging way for users to access their accounts while significantly reducing the risk of password-related security breaches. The Graphical Password Authenticator system ensures robust security through a combination of graphical recognition and encryption technologies. It is designed to be resistant to common attack methods such as brute force and keyloggers, providing a higher level of protection for user accounts and sensitive information. This novel approach to user authentication fosters a secure and user-centric environment, making it a promising solution for the ever-evolving digital landscape. The Graphical Password Authenticator is a groundbreaking step at the intersection of user authentication and innovation. By redefining the way users prove their identity through memorable graphical elements, this approach empowers users with a more secure and intuitive means of accessing their digital resources. This paradigm shift in authentication methods showcases the potential of technology to enhance user security and create a safer digital future, ultimately bridging the gap between user convenience and robust cybersecurity.

“Graphical Password Authentication using Blockchain”

Abstract: This paper proposes a novel approach to authentication by integrating graphical passwords with blockchain technology. By storing authentication data on the blockchain, we aim to enhance security and accountability. Our system offers tamper-proof records and decentralized authentication, contributing to improved security in digital systems.

A Recognition-Based Graphical Password System for Enhanced User Authentication

Abstract: Password-based authentication is a popular way for ensuring computer application security and privacy. Nonetheless, the "weakest link" in the authentication process is believed to be user-chosen weak passwords and hazardous input techniques. People generally utilize mnemonic or brief passphrases instead of random alphanumeric characters. Because internet and mobile applications are widely available, users can access them from any location, at any time, using any device. While this simplicity is generally appreciated, it also increases the likelihood that credentials will be compromised through Visual Hacking. The assault tactics can include employing external recording equipment or watching the victim personally in order to obtain their login information. In response to this issue, our team created a graphical password-based authentication mechanism to combat the threat of Visual Hacking. The system's security mechanism is three-tiered, featuring password verification as well as colour and pattern matching features. This means that even with numerous camera-based hacking attempts, potential attackers would have a tough time determining or limiting the password. In addition, we created and tested a system prototype to determine its usability. According to our trial results, our system can survive Visual Hacking better than previous authentication systems while maintaining a high level of security and usability.

Interactive AI Chatbot for Mental Illness

In today's increasingly technological world, mental health awareness is paramount, with the intersection of technology and wellness offering innovative solutions to alleviation of depression symptoms, attrition, and loss of follow-up in mental health treatment. To start a good life healthcare is more important. Mental health problems becoming more common, there's a need for easy-to-access support. Our chatbot uses smart technology to understand what users are feeling, offer kind responses. It's built using advanced computer learning to get better at recognizing and dealing with different mental health concerns. In this application implement natural language processing (NLP) and speech recognition. The application includes a robust authentication system for user security, featuring traditional methods like username/password verification, alongside a graphical password authentication system. Users can select a random image during registration, which is then split into shares for secure storage. The chatbot analyzes user queries to recommend tailored stress-relief strategies, including mindfulness exercises, relaxation techniques, and personalized yoga poses displayed with accompanying images and instructions. The backend manages user data, recommendation algorithms, and integration with external services, while the frontend offers intuitive interfaces for seamless user interaction. Thorough testing ensures functionality, usability, and security before deployment, with continuous improvement based on user feedback and technological advancements driving the project evolution.

A Graphical Password Authentication System

Graphical passwords provide a promising alternative to traditional alphanumeric passwords. They are attractive since people usually remember pictures better than words.In this extended abstract, we propose a simple graphical password authentication system. We describe its operation with some examples, and highlight important aspects of the system.

Securing Digital Vaults: Advanced Tactics for Protecting Personal Information Online

Abstract: This research explores the critical significance of robust password security in protecting personal and sensitive data on digital platforms. It emphasizes key factors contributing to password vulnerability, including length, structure, recycling, and irregular updates. The paper stresses the paramount importance of creating strong, unique passwords while avoiding personal details and refraining from password reuse. Additionally, it discusses advanced techniques like the Honeyword technique and HoneyEncryption to enhance password security. User education on mobile authentication and preventing shoulder-surfing attacks is highlighted. Privacy implications related to sensitive personal data usage are also examined. Moreover, the potential benefits of graphical passwords in improving security and user convenience are explored. Ultimately, this paper provides valuable insights and practical recommendations for individuals and organizations to prioritize and strengthen password security, thus safeguarding their personal information online.

PASS POINT SELECTION OF AUTOMATIC GRAPHICAL PASSWORD AUTHENTICATION TECHNIQUE BASED ON HISTOGRAM METHOD

Graphical passwords, as opposed to textual passwords, require the user to pick pictures or draw symbols rather than input written letters. They are an option that may be explored in order to get over the issues that are caused by the system of passwords that are based on text. It has been hypothesized that graphical passwords are more difficult to crack using a brute force technique or to figure out through guessing. This paper proposes an authentication system based on a graphical password method. The proposed system computes the password points using histogram arithmetic and encrypts the chosen password points using SHA512. The envisioned system has been realized as an android application and evaluated with existing research considering multiple measurements such as required login time, password space, and entropy. The findings reveal that the new suggested system outperforms the reference work by more than 85% in terms of login latency and more than 72% in terms of entropy results.

A Systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

A Systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

Corrections to “A Systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords”

Corrections to “A Systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords”

Graphical Password Scheme Based on Color Hint Approach

Generally, textual password is the most used kind of authentication technique. However, this authentication type is vulnerable to a lot of attacks. Users using this kind of authentication techniques are at the risk of exposing vital organizational data or even password to attacks such as phishing, shoulder surfing, brute force and dictionary attacks etc. This is the major reason why a new authentication type is required, in other to minimize these threats or attacks, graphical authentication techniques were developed. But this authentication also faces attacks such as shoulder surfing and guessing attacks etc. In this paper, a graphical password scheme based on color hint approach has been developed and analyzed. The scheme authenticates users in two different ways which are: using an image to select different password click points and using secret color hint and transparent shapes as a way for the user to know the position of the click points after it had been reshuffled on the selected image. The project will be limited to the reliability, efficiency, ease of use and friendliness of graphical user interface during the user authentication. The system has been able to minimize or reduce the problem of shoulder surfing and guessing attacks.