Task-Aware Information Hiding in Wireless AI/ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI/ML models for information hiding in wireless communications are task unaware, leading to inefficiencies in data embedding and retrieval, and suffer from shortcomings in similarity metrics that fail to account for task-dependent distortions.
Innovation Solution
The development of task-aware information hiding AI/ML models that embed information in a host data container while being mindful of subsequent AI/ML tasks, using an adjustment function to focus on similarity metrics and combining losses to produce a joint loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing AI/ML models are used for information hiding, then information can be embedded in host data, but the models are task unaware leading to inefficiencies in data embedding and retrieval
Solution Approach 1:
The patent modifies the loss function parameters by introducing task-aware components that adjust the embedding process based on the specific AI/ML task. The joint loss function combines traditional information hiding objectives with task-specific performance metrics, enabling the model to adapt embedding strategies according to task requirements while maintaining embedding efficiency.
Solution Approach 2:
The patent implements dynamic adjustment of embedding parameters based on task characteristics. The model dynamically modifies embedding strength, location, and method according to the specific AI/ML task being performed, transforming static embedding approaches into adaptive, task-responsive processes that optimize both efficiency and task performance.
2Measurement precision
If existing AI/ML models are used for information hiding, then information can be embedded in host data, but they suffer from shortcomings in similarity metrics that fail to account for task-dependent distortions
Solution Approach 1:
The patent applies local quality by introducing task-specific similarity metrics that evaluate different regions or aspects of the embedded data differently based on task requirements. Instead of using a uniform similarity metric, the model adjusts measurement criteria locally according to task-dependent importance, ensuring accurate evaluation where it matters most for each specific task.
3Quantity of substance
If information is embedded in host data using existing methods, then data capacity can be increased, but security and robustness against distortions and steganalysis are compromised
Solution Approach 1:
The patent implements feedback mechanisms where the model continuously evaluates the impact of embedded information on host data integrity and task performance. The joint loss function provides feedback that balances embedding capacity with robustness requirements, adjusting embedding strategies in real-time to maintain security against distortions and steganalysis while maximizing data capacity.
Solution Approach 2:
The patent prepares the embedding process in advance by training the model with anticipated distortions and attacks. The model is pre-conditioned to withstand expected channel distortions and steganalysis attempts, cushioning against future reliability issues before they occur by incorporating robustness considerations into the embedding design from the outset.
Data Source
AI summary
Techniques pertaining to task-aware information hiding artificial intelligence/machine learning (AI/ML) models used in wireless communications are described. An apparatus performs task-aware information hiding or partial task-aware information hiding using an information hiding AI/ML model to embed information in a host data as a container. The apparatus then communicates with a network using the container which contains the embedded information.