Neural Network Information Hiding and Detection
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Solution Overview
Problem
Current neural network technologies lack efficient methods for hiding and detecting information within data items like images or audio signals without human perception, and require manual feature and encoding schemes, which are not scalable or computationally efficient.
Innovation Solution
A system utilizing trained neural networks to modify data items using adversarial examples, allowing them to be classified as belonging to specific classes, thereby embedding and detecting information automatically, without manual tuning, and applicable to various signal types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual feature and encoding schemes are used for information hiding, then information can be embedded in data items, but the process is not scalable and computationally inefficient
Solution Approach 1:
The neural network automatically learns feature representations and encoding schemes through training on classification tasks, eliminating the need for manual feature engineering and encoding design. The system self-optimizes the information hiding process by adjusting network parameters during training to maximize classification accuracy while embedding information.
Solution Approach 2:
The patent transforms the information hiding problem into a parameter optimization problem by modifying neural network parameters during training. The encoding process changes the parameters of the input data item (e.g., pixel values in images) to shift the neural network's classification decision, thereby embedding information in the parameters of the data rather than through manual feature manipulation.
2Adaptability or versatility
If adversarial examples are used to modify data items, then information embedding capability is improved, but device complexity increases
Solution Approach 1:
The same neural network architecture serves dual functions: it acts as both the encoder (modifying data items to embed information) and the decoder (detecting embedded information through classification). This universal approach eliminates the need for separate specialized hardware or algorithms for encoding and decoding, reducing overall system complexity despite the sophisticated adversarial example techniques employed.
3Device complexity
If a single neural network architecture is used for both encoding and decoding, then hardware needs are reduced, but the system must handle multiple functions simultaneously
Solution Approach 1:
The neural network is designed with universal functionality to perform both encoding and decoding operations. During training, the network learns to modify inputs for information hiding while maintaining its classification capability. The same weights and architecture enable both functions, reducing hardware requirements while preserving full functional flexibility through parameter optimization rather than structural duplication.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for hiding information using neural networks. One of the methods includes maintaining data mapping each of a plurality of classes to a respective piece of information that may potentially be hidden in a received data item; receiving a new data item; receiving data identifying a first piece of information to be hidden in the new data item; and modifying the new data item to generate a modified data item that, when processed by a neural network configured to classify input data items belonging to one of the plurality of classes, is classified by the neural network as belonging to a first class of the plurality of classes that is mapped to the first piece of information in the maintained data.