Deep Learning Brand Recognition for Spoofed Content Detection
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Solution Overview
Problem
Existing security solutions are inadequate in effectively identifying and blocking sophisticated brand spoofing attacks, which deceive users by mimicking legitimate brands to obtain sensitive information.
Innovation Solution
A computer system and method that autonomously generates a brand registry by encoding indicators from brand content as vectors, identifying clusters, and determining brand indicators, allowing for the classification of unknown content as real or fake based on similarity to registered brands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security solutions (antivirus software, email filters) are used, then device complexity is reduced and ease of operation is maintained, but detection precision and reliability are insufficient for sophisticated brand spoofing attacks
Solution Approach 1:
The patent introduces an intermediary deep learning-based brand recognition system that acts as a mediator between traditional security solutions and brand spoofing detection. This intermediary layer processes visual content through neural networks to identify brand authenticity, thereby improving detection precision without requiring complete redesign of the security infrastructure. The intermediary handles the complex analysis while traditional solutions maintain their operational simplicity.
Solution Approach 2:
The patent replaces traditional rule-based and signature-based detection mechanisms with deep learning-based visual recognition. Instead of relying on predefined patterns and manual security rules, the system uses neural networks to automatically learn and recognize brand visual characteristics, thereby improving detection capability against sophisticated spoofing attempts while reducing reliance on complex manual configuration.
2Reliability
If advanced deep learning-based brand recognition is implemented, then detection precision and reliability improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-training deep learning models on extensive brand visual data before deployment. Brand indicators, visual characteristics, and authentication patterns are learned and stored in advance, allowing the system to make reliable detection decisions during runtime without requiring complex real-time computation. This pre-processing reduces operational complexity while maintaining high detection reliability.
Solution Approach 2:
The patent transitions from traditional one-dimensional text-based or signature-based detection to multi-dimensional visual feature analysis. By examining brand logos, color schemes, typography, layout patterns, and other visual dimensions simultaneously, the system achieves higher reliability through comprehensive analysis. This dimensional expansion is handled by the deep learning architecture, which manages the complexity of processing multiple features in parallel.
3Measurement precision
If manual data labeling and brand registration are required, then measurement precision can be maintained, but productivity and ease of manufacture deteriorate due to time-consuming manual processes
Solution Approach 1:
The patent implements self-service through automated brand registration and data labeling using unsupervised and semi-supervised learning techniques. The system automatically clusters brand content, extracts visual features, and generates brand indicators without requiring manual annotation for each new brand. This self-service capability maintains measurement precision through consistent automated processing while dramatically improving productivity by eliminating time-consuming manual labeling processes.
Solution Approach 2:
The patent uses copying by replicating brand visual characteristics from authentic sources to create training data and reference patterns. Instead of manually creating labeled datasets, the system collects and processes authentic brand content, extracts their visual features, and uses these copied patterns as the basis for detection. This approach preserves measurement precision by using genuine brand materials while improving productivity through automated feature extraction and pattern generation.
Data Source
AI summary
A computer system and method are provided for generating a brand registry and classifying content as real or fake based on the brand registry. The brand registry is formed by generating a representation of brand content by encoding indicators found in brand content as a vector, identifying clusters in the encoded brand content as separate brands, and determining brand indicators for each brand. Unknown content is classified as real or fake brand content by encoding the unknown content, finding as the most similar brand the brand in the brand registry having a cluster centroid closest to the encoded unknown content, and comparing representative indicators for the unknown content to brand indicators for the most similar brand.