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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

As interest in deep neural networks (DNNs) for image reconstruction tasksgrows, their reliability has been called into question (Antun et al., 2020;Gottschling et al., 2020). However, recent work has shown that, compared tototal variation (TV) minimization, when appropriately regularized, DNNs showsimilar robustness to adversarial noise in terms of $\ell^2$-reconstructionerror (Genzel et al., 2022). We consider a different notion of robustness,using the $\ell^\infty$-norm, and argue that localized reconstruction artifactsare a more relevant defect than the $\ell^2$-error. We create adversarialperturbations to undersampled magnetic resonance imaging measurements (in thefrequency domain) which induce severe localized artifacts in the TV-regularizedreconstruction. Notably, the same attack method is not as effective against DNNbased reconstruction. Finally, we show that this phenomenon is inherent toreconstruction methods for which exact recovery can be guaranteed, as withcompressed sensing reconstructions with $\ell^1$- or TV-minimization.

Localized adversarial artifacts for compressed sensing MRI