Initialization of random vectors to enhance Defense GAN for image classification

keyword: Adversarial attacks

LCSH: Generative adversarial networks (Computer networks)

; White-box attack

LCSH: Digital images -- Classification

LCSH: Machine learning

LCSH: Computer networks -- Safety measures

LCSH: Deep learning (Machine learning)

Abstract: Deep learning has been successfully applied to a wide spectrum of applications. Nevertheless, its performance is seriously degraded by adversarial attacks, an elaborately designed small perturbation to input data that can mislead the model prediction. Specifically, life-critical deployments of deep learning such as autonomous vehicles are a major concern of these attacks. Defense-GAN is considered a state-of-the-art defense method that is robust to both black-box and white-box adversarial attacks. Its defense framework generates new data close to the unperturbed ones from a given adversarial sample before feeding to the predictive model. But Defense-GAN takes time to determine reconstructed data at inference time due to the iterative nature of gradient descent. In this work, the improvement of Defense-GAN to rapidly generate well-reconstructed data from adversarial samples is proposed using carefully crafted initialization of random vectors. The experimental results demonstrate that the proposed method can outperform both classification performance and inference time compared with the Defense-GAN on the benchmark datasets.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2022

Modified: 2025-08-22

Issued: 2025-08-22

บทความ/Article

application/pdf

BibliograpyCitation : In Electrical Engineering Academic Association (Thailand) and Rajamangala University of Technology Isan Khon Kaen Campus. Faculty of Engineering. The 2022 International Electrical Engineering Congress (iEECON 2022) (P01885). Khon Kaen : Rajamangala University of Technology Isan, 2022

eng

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