Adversarial Robustness of Deep Convolutional Candlestick Learner

TL;DR

This paper proposes a method to enhance the robustness of deep learning models used for financial candlestick classification against adversarial perturbations, addressing a key vulnerability for critical applications.

Contribution

It introduces a novel approach to generate perturbed examples that improve the stability of DL models in financial trading scenarios.

Findings

Increased model stability against input perturbations

Effective method for adversarial training in financial data

Enhanced robustness without sacrificing accuracy

Abstract

Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called \emph{adversarial attacks}. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of constructing perturbed examples and use these examples to boost the robustness of the model. Our algorithm increases the stability of DL models for candlestick classification with respect to perturbations in the input data.