Adversarial Perturbation Attack Sensitivity Visualization for Deep Learning
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Solution Overview
Problem
Current mechanisms lack the ability to quantify the effects of adversarial perturbations on deep learning computing systems, making it difficult to identify which types of perturbations are most influential and how to effectively harden these systems against such attacks.
Innovation Solution
An adversarial perturbation attack sensitivity (APAS) visualization system is implemented, which receives natural and adversarial input datasets to determine sensitivity measures and generate classification activation maps (CAMs) and sensitivity overlays, providing a graphical visualization of perturbation sensitivities to help users understand and mitigate vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained to achieve high accuracy on natural inputs, then classification performance is improved, but vulnerability to adversarial perturbations increases
Solution Approach 1:
The system performs preliminary analysis by generating classification activation maps (CAMs) and sensitivity overlays before final classification decisions. This allows identification of vulnerable regions in the input data that are susceptible to adversarial perturbations, enabling preprocessing or mitigation actions before the model makes its classification decision.
Solution Approach 2:
The patent introduces classification activation maps and sensitivity overlays as intermediary visualizations between the input data and the model's classification output. These intermediaries reveal which regions of the input most influence the classification decision, allowing developers to identify and mitigate vulnerabilities without changing the core model architecture.
2Reliability
If adversarial perturbation attacks are applied to test model vulnerability, then security assessment is improved, but understanding of model behavior becomes more complex
Solution Approach 1:
The system uses color-coded classification activation maps and sensitivity overlays to visually represent different aspects of model behavior. Different colors indicate different levels of sensitivity to perturbations, allowing complex security assessment results to be communicated through intuitive visual patterns that are easier to interpret than numerical data alone.
Solution Approach 2:
The patent transforms complex perturbation sensitivity data into two-dimensional visual representations through CAMs and overlays. This dimensional transformation allows security assessors to perceive patterns and vulnerabilities that would be difficult to detect in high-dimensional numerical data, simplifying the interpretation of security assessment results.
3Measurement precision
If sensitivity analysis is performed at fine granularity, then identification of vulnerable regions is improved, but computational cost increases
Solution Approach 1:
The system segments the input data into distinct regions and generates separate classification activation maps for different segments. This allows sensitivity analysis to be performed at fine granularity for each segment while managing overall computational cost by processing segments independently rather than analyzing the entire input as a single unit.
Data Source
AI summary
Mechanisms are provided for generating an adversarial perturbation attack sensitivity (APAS) visualization. The mechanisms receive a natural input dataset and a corresponding adversarial attack input dataset, where the adversarial attack input dataset comprises perturbations intended to cause a misclassification by a computer model. The mechanisms determine a sensitivity measure of the computer model to the perturbations in the adversarial attack input dataset based on a processing of the natural input dataset and corresponding adversarial attack input dataset by the computer model. The mechanisms generate a classification activation map (CAM) for the computer model based on results of the processing and a sensitivity overlay based on the sensitivity measure. The sensitivity overlay graphically represents different classifications of perturbation sensitivities. The mechanisms apply the sensitivity overlay to the CAM to generate and output a graphical visualization output of the computer model sensitivity to perturbations of adversarial attacks.