Adversarial Sampling for Fairness Testing in Deep Neural Network

• URL: http://arxiv.org/abs/2303.02874v1
• Date: Mon, 6 Mar 2023 03:55:37 GMT
• Title: Adversarial Sampling for Fairness Testing in Deep Neural Network
• Authors: Tosin Ige, William Marfo, Justin Tonkinson, Sikiru Adewale, Bolanle Hafiz Matti
• Abstract summary: adversarial sampling to test for fairness in prediction of deep neural network model across different classes of image in a given dataset. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy.

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