# Robustness Of Saak Transform Against Adversarial Attacks

Source: [Robustness Of Saak Transform Against Adversarial Attacks](<https://www.sota2.com/research/paper/68fa65521e209131cea099fe>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:23:52.793Z

Authors: Thiyagarajan Ramanathan, Abinaya Manimaran, Suya You, C-C Jay Kuo

Published: 2019-02-07T20:11:03.000Z

## Abstract

Image classification is vulnerable to adversarial attacks. This work investigates the robustness of Saak transform against adversarial attacks towards high performance image classification. We develop a complete image classification system based on multi-stage Saak transform. In the Saak transform domain, clean and adversarial images demonstrate different distributions at different spectral dimensions. Selection of the spectral dimensions at every stage can be viewed as an automatic denoising process. Motivated by this observation, we carefully design strategies of feature extraction, representation and classification that increase adversarial robustness. The performances with well-known datasets and attacks are demonstrated by extensive experimental evaluations.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.02826>)

[PDF](<https://arxiv.org/pdf/1902.02826.pdf>)