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

In recent years, generative adversarial networks (GANs) and its variants haveachieved unprecedented success in image synthesis. They are widely adopted insynthesizing facial images which brings potential security concerns to humansas the fakes spread and fuel the misinformation. However, robust detectors ofthese AI-synthesized fake faces are still in their infancy and are not ready tofully tackle this emerging challenge. In this work, we propose a novelapproach, named FakeSpotter, based on monitoring neuron behaviors to spotAI-synthesized fake faces. The studies on neuron coverage and interactions havesuccessfully shown that they can be served as testing criteria for deeplearning systems, especially under the settings of being exposed to adversarialattacks. Here, we conjecture that monitoring neuron behavior can also serve asan asset in detecting fake faces since layer-by-layer neuron activationpatterns may capture more subtle features that are important for the fakedetector. Experimental results on detecting four types of fake facessynthesized with the state-of-the-art GANs and evading four perturbationattacks show the effectiveness and robustness of our approach.

FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces