12 results about "Cheating" patented technology

The present application relates to a kind of network security CTF competition multi-class cheating identification and trace analysis method, belong to network security technical field.The present application first monitors and records log to the process of player competition answering question;Then use finite state machine to analyze the logic abnormality of player of answering question step, based on normal distribution to judge the time abnormality of answering question step of player, and according to flag submission time, propose player correlation degree calculation method, find suspicious high similarity player, analyze and trace back the source of player cheating;Finally, collect and comprehensively judge suspicious player Writeup, answering question log under new and old environment, output cheating identification result and analysis report.The method effectively identifies the cheating behaviors in CTF competition, such as proxy participation, multiple accounts, plagiarism of other players' flags or solution ideas, and locates suspicious similar players.

The present disclosure relates to the technical field of computer, and discloses a cheating behavior identification method and device, a storage medium and an electronic device. The method comprises: establishing a full-duplex communication protocol Websocket connection with a target client; receiving first game operation information fed back by the target client within a first preset time period based on the Websocket connection, and calculating the untrustworthiness of the target client according to the first game operation information and corresponding second game operation information, wherein the first preset time period is the time required for the target service node to respond to the current game operation request of the target client, and the second game operation information is preset operation reference information; in response to the untrustworthiness meeting a first preset identification condition, outputting a first identification result, wherein the first identification result is used to indicate that the user corresponding to the target client has cheating behavior. The present disclosure can improve the accuracy and real-time performance of detecting cheating behavior.

Examples herein include systems for detecting cheating and changes in mental performance in card games, including bridge. The system can acquire board data for multiple events, the events including bridge games. For each event, determining performance values for a player based on the board and play data, wherein the performance values may additionally be based in part on timing data. The system can detect a deviation of by comparing the performance values against a threshold, wherein the threshold is based on past performance of known cheating players or the individual player. Then the system can alert a user when the likelihood of cheating or other changes exceeds a threshold.

The invention discloses a multi-level suspected cheating risk calculation method and device and a computer readable storage medium. The multi-level suspected cheating risk calculation method comprises the steps of determining an examinee suspected cheating rate corresponding to each examinee in a target examination room; determining an examination room suspected cheating rate of the target examination room according to the examinee suspected cheating rate; according to the examination room suspected cheating rates corresponding to the subordinate target examination rooms of the examination room, determining an examination room suspected cheating rate; determining the suspected cheating rate of the examination area according to the examination site suspected cheating rates corresponding to the examination sites in the examination area; and determining a region suspected cheating rate according to the examination area suspected cheating rates corresponding to the examination areas administered by the region management center. Therefore, by constructing a multi-level risk aggregation mechanism of examinees, examination rooms, examination sites, examination areas and area management centers, identified individual abnormal behaviors are fused into group risk indexes level by level, and risk level division, weighted aggregation and dynamic calibration are introduced into each level. Therefore, systematic risk calculation from local detection to global evaluation is realized.

The invention discloses a fish scale anti-cheating method and system based on multi-dimensional feature analysis and machine learning, and belongs to the technical field of electronic weighing instruments. The method comprises the following steps: acquiring an original weight signal sequence in a weighing process; effective weighing data segments are positioned through intelligent data cleaning; a ten-dimensional feature vector containing basic fluctuation, dynamic change and composite statistical features is extracted from the data segment; and performing classification prediction by using a pre-trained random forest model, and filtering and outputting a final result based on confidence. The system comprises a prediction service module, a core prediction module and a machine learning training module. According to the invention, through combination of multi-dimensional feature analysis and machine learning, the industrial problem that a completely static cheating object is difficult to identify in the prior art is effectively solved, and the accuracy, robustness and reliability of anti-cheating detection are significantly improved.

The invention discloses a consistency inference anti-cheating sign-in method and a consistency inference anti-cheating sign-in system based on cross-modal feature collaboration, and belongs to the technical field of identity verification. The method comprises the following steps of: acquiring original features of human faces, voiceprints, positioning and actions in real time through a multi-modal terminal, and mapping the original features into feature embedding vectors with uniform dimensions by using a feature encoder; and the system preferentially checks identity consistency, constructs a cross-modal time sequence feature matrix after verification is passed, and extracts feature variations of positioning and actions of adjacent nodes to screen suspected nodes. And introducing a sliding monitoring mechanism and a cross-modal conflict index evaluation model, dynamically accounting a local cheating risk score in combination with a suspicion frequency, and comparing the local cheating risk score with a blocking threshold, so as to identify and intercept cheating behaviors such as virtual positioning and video signing on behalf. According to the method, the limitation of a single mode is broken through, behavior logic abnormity is accurately identified, the false alarm rate of a complex scene is effectively reduced, and a high-robustness sign-in management closed loop is realized.

The invention discloses an online examination system, and relates to the technical field of online examination systems. The intelligent question bank generation module is used for automatically generating a question bank by utilizing an intelligent algorithm according to knowledge points, difficulty coefficients and question type information; meanwhile, according to historical answering conditions of the examinees, difficulty and types of questions in the question bank are dynamically adjusted so as to adapt to requirements of different examinees; the intelligent invigilation module is used for monitoring behaviors of examinees in real time through a computer vision technology and automatically identifying and recording suspected cheating behaviors; meanwhile, the natural language processing technology is utilized to analyze the voice of the examinee in real time, and whether the cheating suspicion exists or not is judged. Through the machine learning algorithm, cheating behaviors can be effectively identified and prevented, the fairness of the examination is ensured, and in addition, the scoring accuracy and the reliability of the system are further improved through the application of the auto-encoder and the integrated learning technology.

The invention belongs to the technical field of intelligent logistics, and particularly relates to an intelligent logistics waybill authenticity inspection model system which comprises the following specific steps: S1, automatically generating a dynamic fence based on waybill information; when the loading and unloading place of the waybill is changed, receiving an updating instruction issued by the platform, completing fence coordinate updating within 10 seconds, and pushing a fence change prompt to a driver end to avoid card punching failure caused by place change; s2, acquiring key original data of a waybill full life cycle from multiple dimensions, constructing a data basic library for waybill authenticity inspection, and ensuring that inspection dimensions cover a waybill performance core link; according to the invention, through the precise spatial constraint of the dynamic electronic fence module and the cooperative verification of the identity sensing unit of the multi-dimensional sensing module, the cheating behaviors of card punching, virtual positioning and waybill progress forgery are completely eradicated.

The invention discloses an anti-cheating monitoring method for an online examination system, and relates to the technical field of examination cheating prevention, audios and videos of an examination room are synchronously collected through a multi-view pickup camera array, and sound source coordinates are positioned through audio noise reduction, human voice screening, time delay inequality and a loudness attenuation rule; the method comprises the following steps: mapping a cheating area to a multi-view video pixel area through space coordinate conversion, unifying the multi-view video pixel area to a main view coordinate system through perspective transformation to realize audio and video cross-modal association, and respectively designing sub-models for audio, face and action features to carry out independent cheating suspicion screening. Three types of feature weights are dynamically distributed through a multi-head attention mechanism to fuse a comprehensive cheating probability, and collective cheating and invigilation cooperative cheating are identified in combination with time sequence correlation analysis; and the cheating judgment result and the audio and video evidence are encrypted and uploaded to the alliance chain for evidence storage, and a non-tampering evidence chain is generated and pushed for graded early warning. According to the method, the cheating identification accuracy and scene adaptability are effectively improved, and technical guarantee is provided for online examination fairness.

The invention relates to a game combat anti-cheating method and device, equipment and a medium. Acquiring combat cheating verification information pushed by the player user side; reproducing a player combat scene based on combat operation instructions corresponding to combat intervals in the combat operation instruction sequence, determining logic layer combat data sets corresponding to the preset combat intervals, and determining anti-cheating fingerprints of the logic layer combat data sets as anti-cheating verification fingerprints by using an anti-cheating fingerprint algorithm; the anti-cheating fingerprints and the anti-cheating verification fingerprints in the same combat interval are compared, and fingerprint abnormal values are counted; and when the counted fingerprint abnormal value exceeds a preset cheating threshold value, executing cheating punishment of the player user side. According to the method, the operation instructions and the logical layer combat data are collected at intervals, so that the combat scene is reproduced, fingerprint comparison is carried out to verify combat anti-cheating, and the combat fairness is improved under the condition that the playing combat experience of players is guaranteed.

The invention provides a game playing method in a competitive confrontation mode based on a block chain, and belongs to the crossing field of privacy protection and incentive mechanisms. According to the method, a competitive confrontation game theory is applied, and behaviors of participants are restrained by using guarantee deposit + random auditing + punishment, so that the expected income of an honest strategy is always greater than that of a malicious strategy. Meanwhile, a long-term income constraint is introduced: a malicious node is subjected to prohibited punishment, and the malicious node is forbidden to continuously participate in a calculation task in a period of time or a plurality of rounds in the future, so that the design can enable a malicious participant to be aware of that single-time cheating loses current guarantee deposit and rewards and also loses the opportunity of long-term continuous profit, and the problem that the malicious participant cannot continuously participate in the calculation task is solved. The opportunity cost of malicious behaviors is greatly improved, so that the occurrence probability of the malicious behaviors is reduced from the source, and a multi-round calculation scene needing long-term stable cooperation is perfectly adapted. In addition, according to the scheme, full-automatic execution of auditing, punishment and reward is achieved through the intelligent contract, and the execution efficiency is improved.

The invention relates to the technical field of game development, in particular to a method for identifying cheating behaviors of players based on big data. The method comprises the steps that a stream processing engine collects player behavior data streams of a game server side in real time; dynamically loading an anti-cheating rule from a preset rule database, and analyzing and judging the player behavior data flow in real time; for the player behavior data flow which is analyzed and judged in real time, extracting multi-dimensional behavior characteristics in the player behavior data flow, and inputting the multi-dimensional behavior characteristics into a pre-trained anomaly detection model set for cheating identification; and on the basis of the real-time analysis and judgment result and / or the cheating identification result, corresponding countering measures are executed, and confirmed cheating cases are fed back to the rule database and / or used for updating the anomaly detection model set. According to the method for identifying the cheating behaviors of the players based on the big data, the self-adaption and learning capabilities of identifying the cheating behaviors of the players are improved.