Visual Text Similarity Detection for Fraud Prevention
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Solution Overview
Problem
Current communication systems lack effective identity validation, making them vulnerable to fraudulent activities, where attackers manipulate message fields to impersonate legitimate senders, leading to data breaches and financial losses, and existing solutions fail to distinguish between legitimate and illegitimate messages without delaying communication flow.
Innovation Solution
A computer-implemented method analyzes input text character strings to identify visually similar strings by using a model of visually similar text characters, calculating a scalar estimate, and determining a visual similarity score to filter and compare candidate strings, thereby identifying the most similar strings without introducing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If identity validation mechanisms are implemented to detect fraudulent messages, then security against fraudulent activities is improved, but communication flow is delayed or impeded
Solution Approach 1:
The system pre-calculates and stores scalar estimates for all text character strings in the corpus before they are needed for fraud detection. This preliminary processing allows the system to quickly compare incoming messages against pre-computed values, enabling rapid identification of visually similar fraudulent messages without delaying communication flow
Solution Approach 2:
The patent introduces scalar estimates as an intermediary representation between the original text messages and the visual similarity comparison process. By converting messages into scalar estimates based on look-alike character sets, the system enables efficient filtering and comparison while maintaining accuracy in detecting fraudulent messages, thus balancing security and communication speed
2Measurement precision
If comprehensive analysis of structured and unstructured data fields is performed to identify fraudulent messages, then detection precision is improved, but processing time increases
Solution Approach 1:
The patent segments the message analysis process into distinct components: extracting structured data fields (from, to, subject) and unstructured data fields (message body), then applying the visual similarity analysis to each segment separately. This segmentation allows comprehensive analysis of all message components while optimizing processing efficiency by handling each segment independently with appropriate methods
Data Source
AI summary
Methods and systems are disclosed for selecting text character strings from a corpus of relevant strings that would commonly be considered to be visually similar to human viewer to an input string. The initial corpus may be any sufficiently broad or specific source of text, e.g., the names of users in a computer application system. The character strings in the corpus are classified such that direct, character-by-character comparisons may be limited to a small subset of likely-similar strings. The input string is then directly compared to strings that are likely to be similar to it, taking into account individual characters' similarities, combinations of characters that look similar to individual characters, transposition of characters, and simple additions and deletions.