CAPTCHA Methods Research Articles

Next-Generation Captcha: Enhancing Security Through Hand Gestures and Gaming

Abstract: In the digital age, the threat of automated attacks on online platforms continues to evolve, necessitating innovative approaches to ensure cybersecurity. Traditional text-based CAPTCHA systems are becoming increasingly vulnerable to sophisticated attacks, prompting the exploration of alternative solutions. This research proposes a novel approach to enhance online security through the integration of hand gestures and gaming elements into CAPTCHA mechanisms. By leveraging the unique biometric characteristics of hand gestures and the engaging nature of gaming, this next-generation CAPTCHA system aims to provide robust protection against automated bots while ensuring a user-friendly experience. The study explores the design, implementation, and evaluation of this hybrid CAPTCHA solution, assessing its effectiveness in mitigating various challenges associated with existing CAPTCHA methods. Through empirical analysis and user feedback, the research aims to demonstrate the feasibility and efficacy of incorporating hand gestures and gaming into CAPTCHA systems to enhance security and usability in online environments.

Recent advances of Captcha security analysis: a short literature review

CAPTCHA has long been used to keep bots from misusing web services. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. In this paper, we presented a short literature review of the current CAPTCHA schemes, as well as highlighting new trends and open issues, the challenges, and the opportunities as a solid starting point for designing the future secure and usable CAPTCHA schemes.

A Novel Design of Audio CAPTCHA for Visually Impaired Users

CAPTCHAs are widely used by web applications for the purpose of security and privacy. However, traditional text-based CAPTCHAs are not suitable for sighted users much less users with visual impairments. To address the issue, this paper proposes a new mechanism for CAPTCHA called HearAct, which is a real-time audio-based CAPTCHA that enables easy access for users with visual impairments. The user listens to the sound of something (the “sound-maker”), and he/she must identify what the sound-maker is. After that, HearAct identifies a word and requires the user to analyze a word and determine whether it has the stated letter or not. If the word has the letter, the user must tap and if not, they swipe. This paper presents our HearAct pilot study conducted with thirteen blind users. The preliminary user study results suggest the new form of CAPTCHA has a lot of potential for both blind and visual users. The results also show that the HearAct CAPTCHA can be solved in a shorter time than the text-based CAPTCHAs because HearAct allows users to solve the CAPTCHA using gestures instead of typing. Thus, participants preferred HearAct over audio-based CAPTCHAs. The results of the study also show that the success rate of solving the HearAct CAPTCHA is 82.05% and 43.58% for audio CAPTCHA. A significant usability differences between the System Usability score for HearAct CAPTCHA method was 88.07 compared to audio CAPTCHA was 52.11%. Using gestures to solve the CAPTCHA challenge is the most preferable feature in the HearAct solution. To increase the security of HearAct, it is necessary to increase the number of sounds in the CAPTCHA. There is also a need to improve the CAPTCHA solution to cover wide range of users by adding corresponding image with each sound to meet deaf users’ needs; they then need to identify the spelling of the sound maker’s word.

IRemember: Memorable CAPTCHA Method for Sighted and Visually Impaired Users

A CAPTCHA is used to automatically differentiate between human users and automated software to prevent bots from accessing unauthorized websites. Most proposed CAPTCHAs are not accessible to visually impaired users because of the memorability of the CAPTCHA’s numerical digits. Recalling six random spoken digits is a difficult task for any human. Visually impaired users must typically play the audio several times to memorize the spoken digits in the correct order. The authors reviewed existing CAPTCHAs for visually impaired users and concluded that the high cognitive load is more susceptible to response errors due to extensive challenge digits intended for visual users. Thus, the authors proposed a novel method that improves current audio CAPTCHA by enhancing the display of the challenge and improving the memorability of its phraseology. The proposed CAPTCHA presents short common phrases, such as “piece of cake.” After hearing or seeing the phrases, the users are required to type the first letter of each word from the presented phrases, such as POC for a piece of cake. The study results of 11 visually impaired users concluded that the memorability and success rate for the IRemember CAPTCHA was 82.72%, compared to the audio CAPTCHA at only 48.18%. It has also demonstrated higher memorability and less workload than the traditional audio method. This research indicates that using common knowledge and experience in the design process for a CAPTCHA method for these users can enhance performance and minimize workload and, hence, error rates.

TICS: text–image-based semantic CAPTCHA synthesis via multi-condition adversarial learning

CAPTCHA is used to distinguish humans from automated programs and plays an important role in multimedia security mechanisms. Traditional CAPTCHA methods like image-based CAPTCHA and text-based CAPTCHA are usually based on word-level understanding, which can be easily cracked due to the recent success of deep learning techniques. To this end, this paper proposes a text–image-based CAPTCHA based on the cognition process and semantic reasoning and a novel model to generate the CAPTCHA. This method synthesizes three features: sentence, object, and location to generate a multi-conditional CAPTCHA that can resist the attack of the classification of CNN. A quantity of experiments has been conducted, and the result showed that the classification of ResNet-50 on the proposed TIC only achieves 3.38% accuracy.

BeCAPTCHA: Behavioral bot detection using touchscreen and mobile sensors benchmarked on HuMIdb

In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology and improve bot detection algorithms. For this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the touchscreen information obtained during a single drag and drop task in combination with the accelerometer data. The goal of BeCAPTCHA is to determine whether the drag and drop task was realized by a human or a bot. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb11https://github.com/BiDAlab/HuMIdb. (Human Mobile Interaction database), a novel multimodal mobile database that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.

A Novel Design of Audio CAPTCHA for Visually Impaired Users

CAPTCHAs are widely used by web applications for the purpose of security and privacy. However, traditional text-based CAPTCHAs are not suitable for sighted users much less users with visual impairments. To address the issue, this paper proposes a new mechanism for CAPTCHA called HearAct, which is a real-time audio-based CAPTCHA that enables easy access for users with visual impairments. The user listens to the sound of something (the “sound-maker”), and he/she must identify what the sound-maker is. After that, HearAct identifies a word and requires the user to analyze a word and determine whether it has the stated letter or not. If the word has the letter, the user must tap and if not, they swipe. This paper presents our HearAct pilot study conducted with thirteen blind users. The preliminary user study results suggest the new form of CAPTCHA has a lot of potential for both blind and visual users. The results also show that the HearAct CAPTCHA can be solved in a shorter time than the text-based CAPTCHAs because HearAct allows users to solve the CAPTCHA using gestures instead of typing. Thus, participants preferred HearAct over audio-based CAPTCHAs. The results of the study also show that the success rate of solving the HearAct CAPTCHA is 82.05% and 43.58% for audio CAPTCHA. A significant usability differences between the System Usability score for HearAct CAPTCHA method was 88.07 compared to audio CAPTCHA was 52.11%. Using gestures to solve the CAPTCHA challenge is the most preferable feature in the HearAct solution. To increase the security of HearAct, it is necessary to increase the number of sounds in the CAPTCHA. There is also a need to improve the CAPTCHA solution to cover wide range of users by adding corresponding image with each sound to meet deaf users’ needs; they then need to identify the spelling of the sound maker’s word.

Secure Captchas via Object Segment Collages

CAPTCHA, a short form of “Completely Automated Public Turing test to Tell Computers and Humans Apart”, is a computer program that can generate and grade tests that most humans can pass, but current computer programs cannot. This paper advances research on image-based CAPTCHA by incorporating the object segment collages. We first collect object segments from thousands of images by using our self-developed collection tool. Then, we create an image collage that compiles randomly selected object segments. To increase the difficulty for computer detection algorithms, we embed some features such as noisy background, object segment occlusion and object segment orientation update. We also develop a practical application using the newly proposed Object Segment Collage CAPTCHA. For the evaluation, we measure the accuracy performance of computer vs. human in solving CAPTCHA questions. In addition, we also conduct a user study over different CAPTCHA methods.

An evaluation of the interactive video CAPTCHA method against automated attack

An evaluation of the interactive video CAPTCHA method against automated attack

An examination of the interactive video CAPTCHA method to resist relay attack

An examination of the interactive video CAPTCHA method to resist relay attack

Improved Captcha Method

CAPTCHA is a program that can tell whether its user is a human or a computer. CAPTCHAs are used by many websites to differentiate between the bots (automated program) and the human. The main motive behind using the CAPTCHA is to prevent abuse from bots, or automated programs usually written to generate spam. The CAPTCHA basically uses images in distorted form that is difficult to read by the bots but they can be read by the human being easily. The websites which provide free services to the users some anonymous user can make false enrollment on the websites with the help of automated computer program. CAPTCAHS are used to prevent the false enrollment. One of the CAPTCHA methods is Collage CAPTCHA. In this method some shapes are shown with distortion and the user is asked to choose a specific object In this paper we increase the resistance of this method to attacks. For this purpose, we show some objects on the left of the screen and some of the objects on the right side of the screen which contain the name corresponding to the images shown on the left side of the screen. Now we ask the user to choose object on the left side and a specific image (containing word) on the right side of the screen. The image on the right side of the screen contains the name of the image. After this user is asked to enter the name of the image in a text box shown below the two images. The user will be passed the test if he chooses the two similar objects correctly and then he/she has entered the name of the image correctly in the text box. In this method because the computer program should also recognize the similar object on the right side and need to enter the name of the image in text box ,the possibility of passing the test by computer is more reduced.