Dual-Filtering System for Adversarial Attack Prevention in ML
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Solution Overview
Problem
Machine-learning systems are vulnerable to adversarial attacks, which existing countermeasures fail to adequately address due to limited scalability and generalization, leaving them without effective protective measures against various types of malicious inputs.
Innovation Solution
A dual-filtering system is implemented, comprising an input filter and an output filter that communicate with each other, using context-sensitive strategies to detect and prevent adversarial attacks by employing multi-objective genetic algorithms for filter sequence optimization and dynamic outlier detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing countermeasures are used to protect ML systems against adversarial attacks, then some protection is provided, but they do not scale well and have low generalization
Solution Approach 1:
The defense system is segmented into two independent filters: an input filter that processes incoming data before the ML model, and an output filter that processes results after the ML model. Each filter can be independently trained and optimized, allowing them to specialize in different aspects of adversarial detection while maintaining overall system generalization capability
Solution Approach 2:
The dual-filter framework is designed to be universally applicable to any ML-based decision support system. The filters can detect various types of adversarial attacks (poisoning, evasion, Trojan AI) across different applications without requiring retraining of the core ML model, providing multi-functional protection
2Reliability
If adversarial defense schemes are implemented, then robustness against malicious attacks increases, but scalability is limited
Solution Approach 1:
The input filter performs preliminary adversarial detection before data reaches the ML model, preventing malicious inputs from consuming computational resources. The output filter performs preliminary verification of model predictions before they are acted upon, enabling scalable deployment across different ML systems without requiring extensive retraining
3Difficulty of detecting and measuring
If adversarial detection schemes are implemented, then detection capability improves, but the system lacks effective protective measures against various types of malicious inputs
Solution Approach 1:
The dual-filter system acts as an intermediary layer between adversarial inputs and the ML model. The input filter mediates by blocking malicious inputs, while the output filter mediates by verifying predictions, providing comprehensive protection that enhances both detection capability and actual protection effectiveness
Data Source
AI summary
A Dual-Filtering (DF) system to provide a robust Machine Learning (ML) platform against adversarial attacks. It employs different filtering mechanisms (one at the input and the other at the output/decision end of the learning system) to thwart adversarial attacks. The developed dual-filter software can be used as a wrapper to any existing ML-based decision support system to prevent a wide variety of adversarial evasion attacks. The DF framework utilizes two filters based on positive (input filter) and negative (output filter) verification strategies that can communicate with each other for higher robustness.