Trans-defense: Transformer-based Denoiser for Adversarial Defense with Spatial-Frequency Domain Representation
Alik Pramanick, Mayank Bansal, Utkarsh Srivastava, Suklav Ghosh, and Arijit Sur

TL;DR
This paper introduces a transformer-based denoising method that combines spatial and frequency domain analysis to improve neural network robustness against adversarial attacks on images.
Contribution
It proposes a novel denoising strategy integrating spatial features with wavelet-based frequency analysis using transformers, enhancing adversarial defense.
Findings
Significant accuracy improvements on MNIST, CIFAR-10, and Fashion-MNIST datasets.
Effective reduction of high-frequency corruption in attacked images.
Enhanced robustness of classifiers through combined denoising and retraining.
Abstract
In recent times, deep neural networks (DNNs) have been successfully adopted for various applications. Despite their notable achievements, it has become evident that DNNs are vulnerable to sophisticated adversarial attacks, restricting their applications in security-critical systems. In this paper, we present two-phase training methods to tackle the attack: first, training the denoising network, and second, the deep classifier model. We propose a novel denoising strategy that integrates both spatial and frequency domain approaches to defend against adversarial attacks on images. Our analysis reveals that high-frequency components of attacked images are more severely corrupted compared to their lower-frequency counterparts. To address this, we leverage Discrete Wavelet Transform (DWT) for frequency analysis and develop a denoising network that combines spatial image features with wavelets…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Generative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection
