Jian Wang

TL;DR: This work proposes a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces, conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector.

TL;DR: The proposed FakeSpotter, based on neuron coverage behavior, in tandem with a simple linear classifier can greatly outperform deeply trained convolutional neural networks (CNNs) for spotting AI-synthesized fake faces.

TL;DR: This work proposes a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces, conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector.

TL;DR: A novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, named motion-based adversarial blur attack (ABBA), which shows more effective penetrating capability to the state-of-the-art GAN-based deblurring mechanisms compared with other blurring methods.

TL;DR: Zhang et al. as discussed by the authors proposed a motion-based adversarial blur attack (ABBA) to generate visually natural motion-blurred adversarial examples, which can be further enhanced by adaptively tuning the translations of object and background.