Fiducial Image Anomaly Detection via Contextual Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current detection techniques fail to effectively protect fiducial payloads from malware and cannot reliably detect anomalies in visual content, such as Deep Fakes, which can deceive users by altering images and videos.
Innovation Solution
A system utilizing machine-learned models to analyze the context of fiducial images, detect anomalies, and provide notifications, by capturing images, determining their context, and identifying anomalies in the background and foreground objects within the images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to detect anomalies in visual content, then detection speed and consistency are improved, but the ability to accurately identify contextual anomalies (such as Deep Fakes) deteriorates because current systems cannot understand scene context
Solution Approach 1:
The patent introduces a trained machine learning model as an intermediary between the visual content and the detection system. This model has been pre-trained on contextual relationships in images and videos, enabling it to automatically detect anomalies that contradict the learned context. The model serves as a bridge that translates visual data into contextual understanding without requiring manual programming of detection rules.
2Measurement precision
If machine-learned contextual models are implemented to detect visual malware and Deep Fakes, then detection accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on extensive datasets of visual content and their contextual relationships before deployment. This pre-training phase establishes the contextual understanding framework in advance, so that during actual operation, the system can quickly detect anomalies by comparing against the pre-established contextual patterns, reducing real-time computational complexity.
3Reliability
If comprehensive contextual analysis is performed on fiducial images to detect malware, then security protection is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the critical contextual features and relationships needed for malware detection from the full image data. Rather than analyzing every pixel and detail, the system identifies and focuses on the specific contextual elements that are most indicative of malicious content, such as inconsistencies between foreground objects and background context, or anomalies in fiducial placement and appearance.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including capturing images generated by a fiducial invoked on a user device; determining a context of the fiducial; detect an anomaly in the images based on the context; and responsive to detecting the anomaly, providing a notification of the anomaly. Other embodiments are disclosed.