Encrypted Delta Reversion for Anti-Learning Content Protection
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Solution Overview
Problem
The unauthorized use of data for training neural networks poses a significant challenge in the context of AI-generated art, as artists lack effective methods to protect their intellectual property from unauthorized exploitation.
Innovation Solution
Intentional data poisoning is employed to create a poisoned data set, which can be reverted to the original using a cryptographically encoded license-key, ensuring only authorized use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is left unprotected for machine learning training, then accessibility and usability of data is improved, but intellectual property rights and authenticity of creative works are compromised
Solution Approach 1:
The patent applies preliminary action by embedding authentication metadata and digital watermarks into data files before they are distributed or used for training. This preemptive measure ensures that provenance information is already present in the data, enabling later verification of authorization without requiring complex real-time authentication systems.
Solution Approach 2:
The patent introduces an intermediary authentication mechanism that acts as a mediator between data providers and machine learning systems. This intermediary layer verifies authorization through embedded metadata and watermarks, allowing data to remain accessible while ensuring proper rights management without direct complex interactions between parties.
2Reliability
If data is protected with authentication mechanisms, then intellectual property rights are safeguarded, but data processing complexity and computational overhead increase
Solution Approach 1:
The patent extracts the authentication verification logic into separate, modular components such as standalone watermarks and metadata structures. This extraction allows the core machine learning processing to remain simple while authentication functions are handled by dedicated, lightweight verification modules that check embedded markers without complicating the main data processing pipeline.
Solution Approach 2:
The patent changes parameters by using subtle, embedded modifications like digital watermarks and metadata fields rather than overt protection mechanisms. These parameter changes are imperceptible to standard data processing operations but provide robust authentication signals, maintaining system simplicity while enhancing security.
3Reliability
If perturbation is introduced to prevent unauthorized training, then data protection is improved, but data quality and utility for authorized uses may deteriorate
Solution Approach 1:
The patent applies local quality by introducing perturbation only in specific, localized regions of the data or in targeted features rather than uniformly across the entire dataset. This selective perturbation maintains high data quality in critical areas while providing sufficient anti-learning protection in vulnerable regions, preserving both data integrity and protection effectiveness.
Solution Approach 2:
The patent creates composite data structures that combine original data with embedded authentication markers and perturbation patterns. This composite approach integrates multiple functions—protection, authentication, and data utility—into a unified structure where each component serves its purpose without significantly degrading the overall data quality or authorized use cases.
Data Source
AI summary
An embodiment receives a first original dataset. The embodiment modifies the first original dataset to create a first poisoned dataset. The embodiment obtains a delta between the first original dataset and the first poisoned dataset. The embodiment encrypts the delta to create an encrypted delta and a corresponding encryption key. The embodiment decrypts, using the encryption key, the encrypted delta to create a decrypted delta. The embodiment reverts, using the decrypted delta, the first poisoned dataset into the first original dataset.