GAN Adversarial Data Estimation for AI Model Recovery
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Solution Overview
Problem
Existing AI models are vulnerable to adversarial attacks, and current approaches lack effective methods for estimating and recovering from such attacks, which can compromise data integrity and reliability.
Innovation Solution
A generative adversarial network (GAN) based system is employed to detect and estimate adversarial data, enabling the classification of input data as real or manipulated, and outputting a difference between the manipulated and original data points, with a trained discriminator and generator using weighted classification and estimated adversarial data losses to enhance detection and recovery capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AI models are used without adversarial detection, then the system operates with simple architecture, but the reliability and data integrity are compromised due to vulnerability to adversarial attacks
Solution Approach 1:
The system segments the AI model into two distinct components: a base AI model for normal processing and a GAN-based adversarial detection system for security. This segmentation allows the main AI model to remain simple while the added security layer handles adversarial detection, thus improving reliability without significantly complicating the core system architecture.
Solution Approach 2:
The GAN-based system acts as an intermediary layer between the input data and the base AI model. It pre-processes incoming data to detect and remove adversarial examples before they reach the main AI model, thereby protecting the system's reliability without requiring modifications to the base model itself.
2Measurement precision
If GAN-based adversarial detection is implemented, then the reliability and detection capability are improved, but the computational complexity and processing time increase
Solution Approach 1:
The GAN-based system performs preliminary detection and filtering of adversarial examples before the main AI model processes the data. By pre-processing the input data to identify and remove malicious samples, the system achieves high detection precision while reducing the computational burden on the main AI model during inference.
Solution Approach 2:
The system uses a GAN framework where a generator creates adversarial examples and a discriminator detects them. This copying mechanism allows the system to train on synthetic adversarial examples rather than requiring extensive real attack data, reducing computational complexity while maintaining high detection precision.
3Loss of information
If the GAN system outputs estimated adversarial data, then the data recovery capability is improved, but the processing time and computational resources increase
Solution Approach 1:
The GAN-based system extracts the adversarial component from contaminated input data by outputting an estimated adversarial data map. This extracted adversarial information can then be removed from the original input to recover the clean data, improving data recovery capability while the extraction process is optimized to minimize processing time.
Data Source
AI summary
A method performed by a generative adversarial network, GAN, based system for outputting an estimated adversarial data, EAD, of an attack on an artificial intelligence, AI, model is provided. The method includes classifying a data point from an input data as (i) a real data point, or (ii) a manipulated data point. The method further includes, when the classification is a manipulated data point, outputting the estimated adversarial data including a difference between the manipulated data point and the data point from the input data. The method may further include using the EAD to build a data recovery module.