Defender Models for Adversarial Attack Detection
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Solution Overview
Problem
Machine learning models are susceptible to adversarial attacks, where malicious input samples with imperceptible perturbations can mislead the models, leading to erroneous outputs, and existing technologies lack effective methods to detect and prevent such attacks.
Innovation Solution
Deploying multiple defender machine learning models at various layers of the target machine learning model, each trained to identify malicious input samples by learning probability density functions of legitimate samples and latent responses, and using negative correlations to detect different types of perturbations, thereby generating a confidence metric to determine the legitimacy of input samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single defender machine learning model is deployed to detect adversarial attacks, then the detection capability is limited, but the system complexity remains low
Solution Approach 1:
The system segments the defense mechanism into multiple specialized defender models (first defender model and second defender model) with different training configurations. Each model focuses on detecting specific types of perturbations, thereby improving overall detection capability while maintaining manageable complexity through modular design
Solution Approach 2:
The defender models are nested within the target machine learning model system, with each defender model operating at different layers. The first defender model is trained on perturbations at one layer while the second defender model is trained on perturbations at a different layer, creating a nested defense structure that comprehensively covers multiple attack vectors
2Measurement precision
If multiple defender models with negative correlations are deployed to detect different perturbation types, then the detection accuracy improves, but the computational resources required increase
Solution Approach 1:
Each defender model is trained with specialized local quality - the first defender model is trained on perturbations specific to its layer while the second defender model is trained on perturbations specific to its different layer. This specialized training enables each model to excel at detecting particular perturbation types with high accuracy while avoiding redundant computation across all models
Solution Approach 2:
The defender models perform preliminary action by detecting and flagging adversarial inputs before they can significantly compromise the target model's output. The negative correlation training ensures that each model is pre-prepared to detect specific perturbation patterns, enabling efficient early detection without requiring exhaustive analysis by all models
Data Source
AI summary
A method for detecting and/or preventing an adversarial attack against a target machine learning model may be provided. The method may include training, based at least on training data, a defender machine learning model to enable the defender machine learning model to identify malicious input samples. The trained defender machine learning model may be deployed at the target machine learning model. The trained defender machine learning model may be coupled with the target machine learning model to at least determine whether an input sample received at the target machine learning model is a malicious input sample and/or a legitimate input sample. Related systems and articles of manufacture, including computer program products, are also provided.