English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent studies have demonstrated that machine learning approaches like deepneural networks (DNNs) are easily fooled by adversarial attacks. Subtle andimperceptible perturbations of the data are able to change the result of deepneural networks. Leveraging vulnerable machine learning methods raises manyconcerns especially in domains where security is an important factor.Therefore, it is crucial to design defense mechanisms against adversarialattacks. For the task of image classification, unnoticeable perturbationsmostly occur in the high-frequency spectrum of the image. In this paper, weutilize tensor decomposition techniques as a preprocessing step to find alow-rank approximation of images which can significantly discard high-frequencyperturbations. Recently a defense framework called Shield could "vaccinate"Convolutional Neural Networks (CNN) against adversarial examples by performingrandom-quality JPEG compressions on local patches of images on the ImageNetdataset. Our tensor-based defense mechanism outperforms the SLQ method fromShield by 14% against FastGradient Descent (FGSM) adversarial attacks, whilemaintaining comparable speed.

TensorShield: Tensor-based Defense Against Adversarial Attacks on Images