An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

TL;DR

This paper introduces a novel, efficient adversarial-based method for explaining deep neural network predictions by analyzing input feature importance through adversarial attacks, enhancing interpretability across various models and datasets.

Contribution

The paper presents a fast, consistent, and easy-to-implement adversarial approach for explaining DNN predictions, demonstrating its generality and effectiveness across multiple tasks and datasets.

Findings

The approach provides consistent explanations for different inputs.

It is faster and more interpretable than existing methods.

Experimental results validate its effectiveness across various models.

Abstract

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.