Neural Network Controller Precision Reduction for Brute Force Protection
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Solution Overview
Problem
Neural networks are vulnerable to unauthorized replication through brute force attacks, as existing security measures like secure elements and homomorphic encryption are either ineffective or degrade performance, and watermarking does not prevent copying.
Innovation Solution
Implementing a neural network controller that reduces the precision of output from the activation function, making it less susceptible to brute force attacks by converting high-precision outputs to lower precision, thereby obscuring changes in node weights and biases, and using a plurality of secured neural network controllers for processing to enhance security and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision output is used from the activation function, then the accuracy and quality of neural network results are improved, but the neural network becomes more vulnerable to brute force attacks and parameter theft
Solution Approach 1:
The patent extracts only the necessary precision level from the high-precision output and removes the excessive precision information that could be exploited for brute force attacks. By outputting only the most significant bits or rounded values, the system retains functional accuracy while eliminating the harmful information that would enable parameter reverse-engineering.
Solution Approach 2:
The patent applies different precision levels to different parts of the output. Critical output values maintain high precision for functional accuracy, while less critical values are output with reduced precision to prevent attack vectors. This localized precision management allows the system to maintain overall accuracy while securing against brute force attacks on specific parameters.
2Reliability
If security measures like homomorphic encryption are implemented, then the security of the neural network is improved, but the computational performance and speed are significantly degraded
Solution Approach 1:
The patent uses inexpensive, computationally lightweight obfuscation techniques instead of heavy cryptographic protocols. By employing simple rounding, bit truncation, or modular arithmetic operations that are computationally cheap, the system achieves security without the performance penalty of expensive encryption schemes like homomorphic encryption.
Solution Approach 2:
The patent changes the precision parameter of the output values to provide security. By adjusting the number of significant bits or the rounding level dynamically, the system can control the balance between security and performance without invoking complex cryptographic transformations that would degrade computational speed.
3Loss of information
If watermarking is used to protect the neural network, then the ability to detect copying is improved, but the prevention of copying is not achieved
Solution Approach 1:
The patent applies preliminary anti-action by preventing the copying process itself through precision reduction, rather than merely detecting it afterward. By making the parameters unrecoverable through reduced precision output, the system proactively prevents successful copying attempts before they can be detected, thereby achieving both detection and prevention capabilities.
Data Source
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AI summary
Herein is disclosed a neural network controller, configured to implement a neural network, the neural network including: a first layer; one or more second layers; and a third layer; wherein each layer of the first layer, the one or more second layers, and the third layer includes one or more nodes; wherein at least one node of the one or more second layers is configured to provide an output value at a first level of precision; wherein the neural network controller is configured to implement a precision reduction function to reduce an output value of at least one node of the third layer to a second level of precision; and wherein the second level of precision is less precise than the first level of precision.