Claims:1. A system (10) for conducting a challenge-response test, wherein the system (10) comprises:
a processing subsystem (20) hosted on a server (30), and configured to execute on a network to control bidirectional communications among a plurality of modules comprising:
a request generation module (40) configured to generate a challenge request on a centralized platform based on predefined criteria, wherein the challenge request corresponds to a request for the user to perform one or more input device-associated tasks;
an input module (50) operatively coupled to the request generation module (40), wherein the input module (50) is configured to receive data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module (40);
a data processing module (60) operatively coupled to the input module (50), wherein the data processing module (60) is configured to:
generate a learning model based on a pre-stored dataset upon receiving the data by the input module (50), wherein the learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique; and
identify the responsive operation performed by the user via the one or more input devices, upon processing the data using the learning model; and
a request matching module (70) operatively coupled to the data processing module (60), wherein the request matching module (70) is configured to:
compare the responsive operation identified by the data processing module (60), with the one or more input device-associated tasks associated with the challenge request generated by the request generation module (40); and
examine the responsive operation for matching with the challenge request generated by the request generation module (40) based on the comparison, thereby conducting the challenge-response test.
2. The system (10) as claimed in claim 1, wherein the challenge request comprises at least one of a text-based challenge request, an audio-based challenge request, and a video-based challenge request.
3. The system (10) as claimed in claim 1, wherein the one or more input device-associated tasks comprises at least one of clicking of a mouse button, pressing one or more keys on a keyboard, inserting a predefined file via one or more input ports, and capturing predefined multimedia via one or more multimedia capturing units.
4. The system (10) as claimed in claim 1, wherein the predefined criteria comprises at least one of a timing of generation, and content to be generated.
5. The system (10) as claimed in claim 1, wherein the responsive operation comprises at least one of clicking of a mouse button, pressing one or more keys on a keyboard, inserting a predefined file via one or more input ports, making one or more movements via one or more body parts of the user, making one or more facial expressions, and uttering a speech.
6. The system (10) as claimed in claim 1, wherein the processing subsystem (20) comprises an alert generation module (140) operatively coupled to the request matching module (70), wherein the alert generation module (140) is configured to generate an alert when the examination of the responsive operation by the request matching module (70) mismatches with the challenge request generated by the request generation module (40),
wherein the alert corresponds to information regarding the user being at least one of an unalert user during a video conference, and a non-human user.
7. A method (190) for conducting a challenge-response test, wherein the method (190) comprises:
generating, by a request generation module (40), a challenge request on a centralized platform based on predefined criteria, wherein the challenge request corresponds to a request for the user for performing one or more input device-associated tasks; (200)
receiving, by an input module (50), data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module; (210)
generating, by a data processing module (60), a learning model based on a pre-stored dataset upon receiving the data by the input module, wherein the learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique; (220)
identifying, by the data processing module (60), the responsive operation performed by the user via the one or more input devices upon processing the data using the learning model; (230)
comparing, by a request matching module (70), the responsive operation identified by the data processing module, with the one or more input device-associated tasks associated with the challenge request generated by the request generation module; and (240)
examining, by the request matching module (70), the responsive operation for matching with the challenge request generated by the request generation module based on the comparison, thereby conducting the challenge-response test (250).
8. The method (190) as claimed in claim 7, wherein generating the challenge request comprises generating at least one of a text-based challenge request, an audio-based challenge request, and a video-based challenge request.
9. The method (190) as claimed in claim 7, wherein performing the one or more input device-associated tasks comprises performing at least one of clicking of a mouse button, pressing one or more keys on a keyboard, providing a predefined file via one or more input ports, and capturing predefined multimedia via one or more multimedia capturing units.
10. The method (190) as claimed in claim 7, comprises generating, by an alert generation module (140), an alert when the examination of the responsive operation by the request matching module mismatches with the challenge request generated by the request generation module, wherein the alert corresponds to information regarding the user being at least one of an unalert user during a video conference, and a non-human user.

Dated this 04th day of June 2021

Signature

Harish Naidu
Patent Agent (IN/PA-2896)
Agent for the Applicant
, Description:FIELD OF INVENTION
[0001] Embodiments of a present disclosure relate to a challenge-response test, and more particularly to a system and method for conducting the challenge-response test.
BACKGROUND
[0002] A challenge-response test is a test in which one party presents a question (challenge) and another party must provide a valid answer (response) to be tested. Generally, the challenge-response test is referred to as a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). Further, the challenge-response test is used for multiple purposes such as, but not limited to, monitoring alertness of a user during a video conference, user authentication, checking if the user is a human or a robot, and the like. Also, there is a plurality of approaches to conducting the challenge-response test. However, such plurality of approaches has limited options and, hence less user-friendly, thereby making such plurality of approaches less secure, less efficient, and less reliable.
[0003] Hence, there is a need for an improved system and method for conducting a challenge-response test which addresses the aforementioned issues.
BRIEF DESCRIPTION
[0004] In accordance with one embodiment of the disclosure, a system for conducting a challenge-response test is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a request generation module configured to generate a challenge request on a centralized platform based on predefined criteria, wherein the challenge request corresponds to a request for the user to perform one or more input device-associated tasks. The processing subsystem also includes an input module operatively coupled to the request generation module. The input module is configured to receive data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module. Further, the processing subsystem also includes a data processing module operatively coupled to the input module. The data processing module is configured to generate a learning model based on a pre-stored dataset upon receiving the data by the input module, wherein the learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique. The data processing module is also configured to identify the responsive operation performed by the user via the one or more input devices, upon processing the data using the learning model. Furthermore, the processing subsystem also includes a request matching module operatively coupled to the data processing module. The request matching module is configured to compare the responsive operation identified by the data processing module, with the one or more input device-associated tasks associated with the challenge request generated by the request generation module. The request matching module is also configured to examine the responsive operation for matching with the challenge request generated by the request generation module based on the comparison, thereby conducting the challenge-response test.
[0005] In accordance with another embodiment, a method for conducting a challenge-response test is provided. The method includes generating a challenge request on a centralized platform based on predefined criteria, wherein the challenge request corresponds to a request for the user for performing one or more input device-associated tasks. The method also includes receiving data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module. Further, the method also includes generating a learning model based on a pre-stored dataset upon receiving the data by the input module, wherein the learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique. Furthermore, the method also includes identifying the responsive operation performed by the user via the one or more input devices upon processing the data using the learning model. Furthermore, the method also includes comparing the responsive operation identified by the data processing module, with the one or more input device-associated tasks associated with the challenge request generated by the request generation module. Furthermore, the method also includes examining the responsive operation for matching with the challenge request generated by the request generation module based on the comparison, thereby conducting the challenge-response test.
[0006] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0007] FIG. 1 is a block diagram representation of a system for conducting a challenge-response test in accordance with an embodiment of the present disclosure;
[0008] FIG. 2 is a block diagram representation of an exemplary embodiment of the system for conducting the challenge-response test of FIG. 1 in accordance with an embodiment of the present disclosure;
[0009] FIG. 3 is a block diagram of a challenge-response test computer or a challenge-response test server in accordance with an embodiment of the present disclosure; and
[0010] FIG. 4 is a flow chart representing steps involved in a method for conducting a challenge-response test in accordance with an embodiment of the present disclosure.
[0011] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION
[0012] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0013] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0015] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0016] Embodiments of the present disclosure relate to a system for conducting a challenge-response test. As used herein, the term “challenge-response test” is defined as a test in which one party presents a question (challenge) and another party must provide a valid answer (response) to be tested. In one embodiment, the challenge-response test may also be termed as Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). Further, the system described hereafter in FIG. 1 is the system for conducting the challenge-response test.
[0017] FIG. 1 is a block diagram representation of a system (10) for conducting a challenge-response test in accordance with an embodiment of the present disclosure. In one embodiment, the challenge-response test may have to be conducted to check alertness of a user in a video conference. In another embodiment, the challenge-response may have to be conducted to check if the user trying to use a predefined system is a human or non-human. The system (10) includes a processing subsystem (20) hosted on a server (30). In one embodiment, the server (30) may include a cloud server. In another embodiment, the server (30) may include a local server. The processing subsystem (20) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as a local area network (LAN). In another embodiment, the network may include a wireless network such as Wireless Fidelity (Wi-Fi), Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID), or the like.
[0018] Basically, in an embodiment, the challenge-response test may be initiated upon generation of a request challenging the user to perform a task. Thus, the processing subsystem (20) includes a request generation module (40) configured to generate a challenge request on a centralized platform based on predefined criteria. The challenge request corresponds to a request for the user to perform one or more input device-associated tasks. In one embodiment, the challenge request may include at least one of a text-based challenge request, an audio-based challenge request, a video-based challenge request, and the like.
[0019] As used herein, the term “text-based challenge request” is defined as a challenge request which appears on a user interface in a form of a text for a user to read the same and perform a task read accordingly. Further, as used herein, the term “audio-based challenge request” is defined as a challenge request which is played in a form of an audio via an audio playing device for a user to hear the same and perform a task heard accordingly. In one embodiment, the audio playing device may include a speaker. Furthermore, as used herein, the term “video-based challenge request” is defined as a challenge request which is played in a form of a video via a video playing device for a user to hear and watch the same and perform a task heard and watched accordingly. In one embodiment, the video playing device may include a display unit, a mobile display, a tablet display, a laptop display, or the like.
[0020] In one exemplary embodiment, the one or more input device-associated tasks may include at least one of clicking of a mouse button, pressing one or more keys on a keyboard, inserting a predefined file via one or more input ports, capturing predefined multimedia via one or more multimedia capturing units, and the like. Further, in an embodiment, as the challenge request may be generated on the centralized platform based on the predefined criteria, the centralized platform may be substantially similar to the system (10). In another embodiment, the centralized platform may be a platform used to control operations of the system (10) by the user via a user interface.
[0021] In one exemplary embodiment, the predefined criteria may include at least one of a timing of generation, and content to be generated. In one embodiment, the timing of generation may include a random basis, a predefined schedule, upon initiation by the user or the like. Basically, in an embodiment, based on the predefined criteria the challenge request may be generated. Thus, in one embodiment, the challenge request may be generated on the random basis which means at any time randomly, the user may be challenged with the challenge request. In another embodiment, the challenge request may be generated based on the predefined schedule. In such embodiment, the predefined schedule may be set by an entity. The entity may include an organizer of a video conference, at least one participant of the video conference, one or more authorities of the predefined system which the user may be using, the user, or the like. In yet another embodiment, the challenge request may be generated upon initiation by the user.
[0022] In one embodiment, the content to be generated may be generated based on at least one of random basis, predefined content, instantly created content by the user, or the like. As used herein, the term “content” is defined as statements asking a user to perform one or more tasks in at least one of a text form, an audio form, a video form, or the like. Thus, in one embodiment, the challenge request may be generated on the random basis which means that the content may be randomly generated by the system (10), and the user may be challenged with the challenge request including the corresponding content generated. In another embodiment, the challenge request may be generated based on the predefined content. In such embodiment, the predefined content may be pre-defined by the entity. In yet another embodiment, the challenge request may be generated upon instantly creating the content by the user.
[0023] Further, upon generating the challenge request, the user may have to respond, and the system (10) may have to be able to receive the same in order to be able to use the same for further processing. Thus, the processing subsystem (20) also includes an input module (50) operatively coupled to the request generation module (40). The input module (50) is configured to receive data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module (40). As used herein, the term “responsive operation” is defined as an operation performed by a user as a response to a question or a challenge. In one embodiment, the data may be stored in a database (as shown in FIG. 2) of the system. In such embodiment, the database may include a local database or a cloud database.
[0024] In one embodiment, the responsive operation may include at least one of clicking of a mouse button, pressing one or more keys on a keyboard, inserting a predefined file via one or more input ports, making one or more movements via one or more body parts of the user, making one or more facial expressions, uttering a speech, and the like. Thus, in one embodiment, the one or more input devices may include at least one of a mouse of a computing device, one or more keys of a keyboard, one or more input ports, one or more multimedia capturing units, and the like. In one embodiment, the computing device may include a mobile phone, a tablet, a laptop, or the like. In one embodiment, the one or more input ports may include a Universal Serial Bus (USB) port, a serial port, a parallel port, microSD card reader, and the like. In one embodiment, the one or more multimedia capturing units may include a camera, an audio recorder, and the like.
[0025] In one embodiment, when the mouse button or the one or more keys on the keyboard may be used by the user to perform the responsive operation, the data may include a control signal. In such embodiment, the control signal may include a digital signal, an electrical signal, a magnetic signal, a current signal, a voltage signal, or the like. In another embodiment, when the one or more input ports may be used by the user to perform the responsive operation, the data may include a predefined file. In such embodiment, the predefined file may be in digital form, wherein the predefined file may include an image, a video, a text-based file, or the like. In yet another embodiment, when the one or more multimedia capturing units may be used by the user to perform the responsive operation, the data may include one or more multimedia including at least one of one or more images, one or more videos, an audio, and the like. In such embodiment, the one or more multimedia may be in digital form.
[0026] Furthermore, upon receiving the data, the data may have to be processed or analyzed in order to identify the responsive operation performed by the user. Thus, the processing subsystem (20) also includes a data processing module (60) operatively coupled to the input module (50). The data processing module (60) is configured to generate a learning model based on a pre-stored dataset upon receiving the data by the input module (50). The learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique. For example, in the case of the keyboard, the operation of each of the one or more keys may be allotted with an alphanumeric character. In such case, for the system (10) to be able to understand the corresponding alphanumeric character, a predefined data such as, but not limited to, a binary value, a series of binary values, or the like with a predefined pattern may be associated with the corresponding alphanumeric character.
[0027] As used herein, the term “machine learning” is defined as an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In one embodiment, the machine learning technique may include at least one of a signal processing technique, an image processing technique, an audio processing technique, a video processing technique, and the like. Basically, in an embodiment, the learning model may be generated upon feeding the pre-stored dataset including one or more instructions according to which the learning model may have to respond or process the one or more inputs received by the learning model.
[0028] As used herein, the term “signal processing technique” is defined as a technique which focuses on analyzing, modifying, and synthesizing signals such as sound, images, and scientific measurements. As used herein, the term “image processing technique” is defined as a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. As used herein, the term “audio processing technique” is defined as a technique that is concerned with the electronic manipulation of audio signals. In one embodiment, the audio processing technique may include storage, data compression, music information retrieval, speech processing, localization, acoustic detection, transmission, noise cancellation, acoustic fingerprinting, sound recognition, synthesis, enhancement, and the like. As used herein, the term “video processing technique” is defined as a technique which is concerned with the extensive processing of videos for the system to understand the content of the corresponding videos.
[0029] Thus, the learning model learns itself the pattern of the predefined data associated with the operation of each of the one or more input devices, now the responsive operation performed by the user via the one or more input devices may have to be identified. Thus, the data processing module (60) is also configured to identify the responsive operation performed by the user via the one or more input devices, upon processing the data using the learning model.
[0030] Moreover, upon identifying the responsive operation performed by the user, the corresponding responsive operation may have to be verified or checked for the same to be in accordance with the challenge request generated initially. Thus, the processing subsystem (20) also includes a request matching module (70) operatively coupled to the data processing module (60). The request matching module (70) is configured to compare the responsive operation identified by the data processing module (60), with the one or more input device-associated tasks associated with the challenge request generated by the request generation module (40). Further, the request matching module (70) is also configured to examine the responsive operation for matching with the challenge request generated by the request generation module (40) based on the comparison, thereby conducting the challenge-response test.
[0031] In one embodiment, when the responsive operation performed by the user does not match with the one or more device-associated tasks, the responsive operation fails to match with the challenge request generated by the request generation module (40) upon examination, thereby generating a negative examination result. In another embodiment, when the responsive operation performed by the user matches with the one or more device-associated tasks, the responsive operation successfully matches with the challenge request generated by the request generation module (40) upon examination, thereby generating a positive examination result.
[0032] In addition, the processing subsystem (20) may also include an alert generation module (as shown in FIG. 2) operatively coupled to the request matching module (70). The alert generation module may be configured to generate an alert when the examination of the responsive operation by the request matching module (70) mismatches with the challenge request generated by the request generation module (40). The alert may correspond to information regarding the user being at least one of an unalert user during the video conference, a non-human user, and the like. In one embodiment, the alert may be generated for the user, an organizer of the video conference, at least one participant of the video conference, one or more authorities of the predefined system which the user may be using, or the like. In one exemplary embodiment, the alert may be generated in one or more forms such as, but not limited to, a text message, an e-mail, a pop-up notification, and the like. In one exemplary embodiment, the predefined system may include a net banking system.
[0033] FIG. 2 is a block diagram representation of an exemplary embodiment of the system (10) for conducting the challenge-response test of FIG. 1 in accordance with an embodiment of the present disclosure. Suppose school authorities of a school ‘S’ (80) are willing to use the system (10) for conducting the challenge-response test to check for the alertness of students (90) while a teacher (100) is teaching via a classroom video conference. The system (10) includes the processing subsystem (20) hosted on the server (30). Further, suppose the teacher (100) has pre-scheduled the generation of the challenge request on the centralized platform with the predefined schedule being after every thirty minutes during each session, via a teacher’s mobile phone (110). Also, suppose the challenge request includes the content which is randomly generated by the system (10). Thus, based on the predefined schedule, the challenge request having the content which is randomly generated may be generated by the system (10) via the request generation module (40).
[0034] Later, upon generation of the request, the students (90) respond for the same. Suppose at one instance, the content included in the challenge request be asking the students (90) to press the Alt+Enter keys of the keyboard, and suppose the challenge request be the text-based challenge request. Thus, upon reading the text-based challenge request, if the students (90) are attentive, then the students (90) press the corresponding keys on the keyboard associated with students’ mobile phone (120) as the responsive operation. Now, the system (10) receives the data corresponding to the responsive operation performed by the students (90) via the input module (50). The data received is stored in the database (130) of the system (10). Further, the data corresponds to the digital signals corresponding to the keys pressed, and hence the system (10) uses the learning model generated by the data processing module (60) in order to identify the keys pressed. Upon identifying the keys pressed by the students (90), the comparison is made with the challenge request for the examination of the responsive operation matching with the challenge request via the request matching module (70). When, the keys identified for at least one of the students (90) mismatches with the challenge request, the teacher (100) is alerted regarding the corresponding at least one of the students (90) being unalert via the alert generation module (140).
[0035] FIG. 3 is a block diagram of a challenge-response test computer (150) or a challenge-response test server (150) in accordance with an embodiment of the present disclosure. The challenge-response test server (150) includes processor(s) (160), and memory (170) operatively coupled to a bus (180). The processor(s) (160), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0036] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (160).
[0037] The memory (170) includes a plurality of subsystems stored in the form of executable program which instructs the processor(s) (160) to perform method steps illustrated in FIG. 4. The memory (170) includes a processing subsystem (20) of FIG 1. The processing subsystem (20) further has following modules: a request generation module (40), an input module (50), a data processing module (60), and a request matching module (70).
[0038] The request generation module (40) is configured to generate a challenge request on a centralized platform based on predefined criteria, wherein the challenge request corresponds to a request for the user to perform one or more input device-associated tasks. The input module (50) is configured to receive data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module (40).
[0039] The data processing module (60) is configured to generate a learning model based on a pre-stored dataset upon receiving the data by the input module (50), wherein the learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique. The data processing module (60) is also configured to identify the responsive operation performed by the user via the one or more input devices, upon processing the data using the learning model.
[0040] The request matching module (70) is configured to compare the responsive operation identified by the data processing module (60), with the one or more input device-associated tasks associated with the challenge request generated by the request generation module (40). The request matching module (70) is also configured to examine the responsive operation for matching with the challenge request generated by the request generation module (40) based on the comparison, thereby conducting the challenge-response test.
[0041] The bus (180) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (180) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (180) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.
[0042] FIG. 4 is a flow chart representing steps involved in a method (190) for conducting a challenge-response test in accordance with an embodiment of the present disclosure. The method (190) includes generating a challenge request on a centralized platform based on predefined criteria, wherein the challenge request corresponds to a request for the user for performing one or more input device-associated tasks in step 200. In one embodiment, generating the challenge request may include generating the challenge request by a request generation module (40). In such embodiment, generating the challenge request may include generating at least one of a text-based challenge request, an audio-based challenge request, a video-based challenge request, and the like.
[0043] In one exemplary embodiment, performing the one or more input device-associated tasks may include performing at least one of clicking of a mouse button, pressing one or more keys on a keyboard, inserting a predefined file via one or more input ports, capturing predefined multimedia via one or more multimedia capturing units, and the like.
[0044] The method (190) also includes receiving data corresponding to a responsive operation performed by the user via one or more input devices upon generating the challenge request by the request generation module in step 210. In one embodiment, receiving the data may include receiving the data by an input module (50).
[0045] Furthermore, the method (190) includes generating a learning model based on a pre-stored dataset upon receiving the data by the input module, wherein the learning model is configured to learn a pattern of predefined data associated with an operation of each of the one or more input devices using a machine learning technique in step 220. In one embodiment, generating the learning model may include generating the learning model by a data processing module (60).
[0046] Furthermore, the method (190) also includes identifying the responsive operation performed by the user via the one or more input devices upon processing the data using the learning model in step 230. In one embodiment, identifying the responsive operation may include identifying the responsive operation by the data processing module (60).
[0047] Furthermore, the method (190) also includes comparing the responsive operation identified by the data processing module, with the one or more input device-associated tasks associated with the challenge request generated by the request generation module in step 240. In one embodiment, comparing the responsive operation with the one or more input device-associated tasks may include comparing the responsive operation with the one or more input device-associated tasks by a request matching module (70).
[0048] Furthermore, the method (190) also includes examining the responsive operation for matching with the challenge request generated by the request generation module based on the comparison, thereby conducting the challenge-response test in step 250. In one embodiment, examining the responsive operation for matching with the challenge request may include examining the responsive operation for matching with the challenge request by the request matching module (70).
[0049] In one exemplary embodiment, the method (190) may also include generating an alert when the examination of the responsive operation by the request matching module mismatches with the challenge request generated by the request generation module, wherein the alert corresponds to information regarding the user being at least one of an unalert user during a video conference and a non-human user. In such embodiment, generating the alert may include generating the alert by an alert generation module (140).
[0050] Further, from a technical effect point of view, the implementation time required to perform the method steps included in the present disclosure by the one or more processors of the system is very minimal, thereby the system maintains very minimal operational speed.
[0051] Various embodiments of the present disclosure enable the conducting of the challenge-response test more user-friendly with multiple options, thereby making the system more secure. Also, as the challenge request and the responsive operation are associated with the one or more input-associated devices, makes the system unique in comparison to the conventional systems.
[0052] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0053] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.