# Applying Tensor Decomposition to image for Robustness against Adversarial Attack Source: [Applying Tensor Decomposition to image for Robustness against Adversarial Attack]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:01:46.445Z Authors: Seungju Cho, Tae Joon Jun, Mingu Kang, Daeyoung Kim Published: 2020-02-28T18:30:22.000Z ## Abstract Nowadays the deep learning technology is growing faster and shows dramatic performance in computer vision areas. However, it turns out a deep learning based model is highly vulnerable to some small perturbation called an adversarial attack. It can easily fool the deep learning model by adding small perturbations. On the other hand, tensor decomposition method widely uses for compressing the tensor data, including data matrix, image, etc. In this paper, we suggest combining tensor decomposition for defending the model against adversarial example. We verify this idea is simple and effective to resist adversarial attack. In addition, this method rarely degrades the original performance of clean data. We experiment on MNIST, CIFAR10 and ImageNet data and show our method robust on state-of-the-art attack methods. ## 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]() [PDF]()