Adversarial Input Purification via Discriminative Region Analysis
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Solution Overview
Problem
Deep learning computing systems are vulnerable to adversarial inputs, which are manipulated data samples that can mislead models into incorrect classifications, posing a security risk by allowing invalid inputs to be misclassified as valid and preventing valid inputs from being correctly classified.
Innovation Solution
The implementation of an adversarial input detection and purification (AIDAP) preprocessor that identifies discriminative regions in input data influencing the deep learning model's output, applies targeted transformations to purify these regions, and compares first and second pass outputs to determine if the input is adversarial, thereby mitigating the effects of adversarial inputs without modifying the deep learning model itself.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models process input data directly without preprocessing, then processing speed is maintained, but the models become vulnerable to adversarial inputs that can mislead classifications
Solution Approach 1:
The AIDAP preprocessor performs preliminary analysis of input data before it reaches the deep learning model. It identifies discriminative regions in the input data that significantly influence model output, and applies targeted transformations to these regions to neutralize adversarial perturbations. This preliminary action protects the model from adversarial inputs while maintaining the overall system architecture.
Solution Approach 2:
The preprocessor segments the input data by identifying discriminative regions rather than processing the entire input uniformly. By focusing computational resources only on the most influential regions of the input data, the system achieves effective adversarial detection with reduced computational overhead compared to processing all data points equally.
2Reliability
If the AIDAP preprocessor transforms elements in discriminative regions, then adversarial inputs are detected and purified, but additional processing time is required
Solution Approach 1:
The preprocessor applies transformations only to locally identified discriminative regions rather than the entire input data. By concentrating processing efforts on specific high-impact regions determined through gradient analysis, the system achieves effective adversarial detection with minimal additional processing time compared to global processing approaches.
Solution Approach 2:
The system performs partial processing by transforming only a subset of elements in the discriminative regions rather than all input elements. This selective approach provides sufficient adversarial protection while avoiding the excessive processing time that would result from comprehensive transformation of the entire input dataset.
3Measurement precision
If the system compares first pass and second pass outputs to detect adversarial inputs, then detection accuracy improves, but computational overhead increases
Solution Approach 1:
The system changes parameters by comparing outputs from two different processing passes (original vs. transformed input). This parameter comparison approach enables precise detection of adversarial inputs by identifying significant output changes, while the transformations are designed to be computationally efficient to minimize energy consumption.
Data Source
AI summary
Adversarial input detection and purification (AIDAP) preprocessor and deep learning computer model mechanisms are provided. The deep learning computer model receives input data and processes it to generate a first pass output that is output to the AIDAP preprocessor. The AIDAP preprocessor determines a discriminative region of the input data based on the first pass output and transforms a subset of elements in the discriminative region to modify a characteristic of the elements and generate a transformed input data. The deep learning computer model processes the transformed input data to generate a second pass output that is output to the AIDAP preprocessor which detects an adversarial input or not based on a comparison of the first pass and second pass outputs. If an adversarial input is detected, a responsive action that mitigates effects of the adversarial input is performed.