VAE Adversarial Resistance Evaluation Using Unlabeled Data
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Solution Overview
Problem
Existing methods for evaluating adversarial attack resistance in variational autoencoders (VAEs) are limited to labeled data, making it impossible to assess resistance when unlabeled data is used as input.
Innovation Solution
An evaluation device and method that includes an input unit for latent variables, a labeling unit for clustering and assigning pseudo-labels, a learning unit for classifier training, and an evaluation unit for assessing adversarial attack resistance, allowing for resistance evaluation even with unlabeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adversarial attack resistance evaluation is performed using conventional methods, then evaluation can be conducted for VAEs with labeled data, but evaluation is impossible for VAEs using unlabeled data
Solution Approach 1:
The invention applies preliminary action by performing clustering analysis on latent variables before the adversarial attack evaluation process. The labeling unit clusters latent variables extracted from unlabeled input data and assigns cluster labels in advance, creating pseudo-labeled datasets that enable subsequent classifier learning and adversarial evaluation. This preliminary clustering action transforms unlabeled data into a format suitable for resistance evaluation.
Solution Approach 2:
The invention introduces an intermediary approach by using clustering-based pseudo-labeling as a bridge between unlabeled data and the requirements of adversarial attack resistance evaluation. The labeling unit acts as an intermediary that generates intermediate representations (cluster labels) from unlabeled latent variables, enabling the evaluation process without requiring original ground truth labels.
2Reliability
If clustering and pseudo-labeling are performed on latent variables, then adversarial attack resistance evaluation becomes possible for unlabeled data, but additional processing steps and complexity are introduced
Solution Approach 1:
The invention applies segmentation by dividing the evaluation process into distinct functional units: an input unit for receiving latent variables, a labeling unit for clustering and assigning labels, a learning unit for training the classifier, and an evaluation unit for performing adversarial attack resistance evaluation. This segmentation allows each component to perform its specific function independently, making the overall complex process more manageable and systematic.
Data Source
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AI summary
An evaluation device receives input of latent variables of a variational autoencoder, clusters the input latent variables, and assigns, for each cluster, a label indicating the cluster to latent variables belonging to the cluster. After that, the evaluation device performs learning of a classifier so as to accurately classify the latent variables based on the assigned label, performs an adversarial attack resistance evaluation for the classifier after learning, and outputs a result of the resistance evaluation. Thus, the evaluation device can perform an adversarial attack resistance evaluation even for a variational autoencoder that uses unlabeled data as input data.