Neural Network Trap Inputs for Dataset Cloning Detection
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Solution Overview
Problem
Existing methods for protecting intellectual property in neural networks, such as legal agreements and code obfuscation, are inadequate as they rely on compliance and do not prevent model cloning effectively.
Innovation Solution
Implementing a 'trap input' during the training phase of deep learning models, which is orthogonal to normal operational data, allowing for real-time detection of cloning by triggering a specific response and embedding source information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legal agreements and code obfuscation are used to protect neural networks, then intellectual property protection is attempted, but model cloning is not effectively prevented
Solution Approach 1:
The patent applies preliminary action by embedding trap inputs during the training phase of the neural network, before deployment. These trap inputs are specially designed data points with known characteristics that will trigger identifiable responses. When the model is later suspected of cloning, these pre-planted traps can be activated to detect whether the model has been copied, providing proactive rather than reactive protection.
Solution Approach 2:
The trap inputs serve as an intermediary mechanism between the model owner and potential clones. Rather than directly monitoring or controlling the model, the trap inputs act as hidden markers embedded within the training data that mediate detection. When activated, these intermediaries reveal whether the model has been unauthorizedly replicated without requiring direct observation of the model's internal state.
2Reliability
If trap inputs are embedded during training, then cloning detection capability is provided, but model performance may be affected
Solution Approach 1:
The patent applies local quality by ensuring that trap inputs have different characteristics from normal operational data. The trap inputs are designed with specific properties (such as unusual feature combinations or marked data points) that make them distinguishable from regular training data. This localized differentiation allows the model to learn from normal data while the trap inputs serve as unique markers, minimizing interference with the model's primary functionality.
Solution Approach 2:
The trap inputs involve parameter changes in the training data space by introducing data points with modified or extreme parameter values that differ from the normal operational range. These parameter changes create detectable signatures in the model's behavior without fundamentally altering the model's ability to process normal inputs, as the trap inputs occupy a different region of the parameter space.
3Ease of manufacture
If traditional protection methods are used, then implementation is simple, but detection of infringement is delayed
Solution Approach 1:
The neural network model with embedded trap inputs essentially serves itself by containing its own detection mechanism. The trap inputs are self-contained markers that the model learns to recognize during training, enabling the model to autonomously provide detection capability without requiring external monitoring systems or complex verification infrastructure. This self-service approach simplifies implementation while enabling timely detection.
Data Source
AI summary
In one aspect, a method includes detecting, by at least one hardware processor, cloning or theft of data set in neural network implementation, for artificial intelligence systems including a deep learning model, training the deep learning model by the data set that is applicable to the current application or product, training the model with a known trap input that is not relevant to the application or data, the known trap input including trap input for generating a different decision output, and employing the dataset to build a model to be used.