Human Activity Tracking for Digital Asset Authenticity Verification
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Solution Overview
Problem
Current systems lack effective methods to ensure the authenticity and legitimacy of human-originated digital assets, particularly in preventing impersonation and cyber-attacks by AI or botnets, which can compromise personal information and assets during transactions over networks.
Innovation Solution
A system and method that captures and verifies human interactions and biometric data during the creation of digital assets, packaging this metadata within the assets for transmission and analysis to distinguish between human-generated and AI-generated content, using monitoring engines and verification processes to ensure authenticity and prevent fraudulent activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intermediate steps are used to ensure transaction legitimacy, then trust between human actors is maintained, but transaction efficiency and automation are reduced
Solution Approach 1:
The system performs preliminary capture and verification of human interaction metadata during the asset creation process itself, rather than requiring separate manual verification steps later. The monitoring engine captures biometric data, device information, and interaction patterns at the time of creation, and this metadata is embedded in the digital asset before transmission, enabling automated verification at the receiving end without requiring manual intervention.
Solution Approach 2:
The system creates a digital copy of human verification characteristics by capturing metadata including biometric data, device identifiers, and interaction patterns during human interaction with the digital asset creation process. This metadata copy is embedded within the digital asset itself, allowing receiving systems to verify authenticity automatically without requiring the original human actor's direct involvement in the verification process.
2Extent of automation
If AI and automated programs are used to build smarter systems, then system intelligence and automation are improved, but vulnerability to impersonation and cyber-attacks increases
Solution Approach 1:
The system introduces metadata as an intermediary layer between the automated system and the human actor. This metadata includes verification characteristics such as biometric data, device information, and interaction patterns that serve as a mediator to prove human involvement. When a digital asset is received, the verification engine uses this metadata intermediary to authenticate whether a human genuinely created the asset, preventing AI or botnet impersonation even in highly automated environments.
Solution Approach 2:
The system applies preliminary anti-action by embedding verification metadata during the asset creation process that specifically counters potential impersonation attacks. The monitoring engine captures human-specific interaction patterns and biometric data before transmission, and the verification engine at the receiving end uses this pre-prepared metadata to detect and reject AI-generated or botnet-created assets, preventing harmful impersonation before it can cause damage.
3Measurement precision
If human interaction metadata is captured and verified, then authenticity of digital assets is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system merges the monitoring engine and verification engine into an integrated authentication framework. The monitoring engine captures metadata during asset creation, embeds it within the digital asset, and the verification engine at the receiving end extracts and verifies this embedded metadata. This merging approach consolidates the complexity into standardized processes that work together seamlessly, reducing the need for separate complex verification systems while maintaining high authenticity verification accuracy.
Data Source
AI summary
A new approach is proposed that contemplates systems and methods to support human activity tracking and authenticity verification of human-originated digital assets. First, activities performed by a producer while he/she is constructing a digital asset, e.g., an electronic message, are captured. Information/metadata of the captured activities are then packaged/encapsulated inside the constructed digital asset, wherein such metadata includes but is not limited to mouse and/or keyboard activities, software tools used, and other digital traces of the captured human activities. Once the digital asset is transmitted and received by a consumer, the metadata included in the digital asset is unpacked and analyzed to determine various levels of authenticity of the digital asset with respect to whether the digital asset is originated manually by a human being or automatically by a software program. The consumer may then take actions accordingly based on the level of authenticity of the received digital asset.