Detecting fake images is becoming a major goal of computer vision. This need is becoming
more and more pressing with the continuous improvement of synthesis methods based on …

The extraordinary ability of generative models to generate photographic images has
intensified concerns about the spread of disinformation, thereby leading to the demand for …

The key objective of Generative Adversarial Networks (GANs) is to generate new data with
the same statistics as the provided training data. However, multiple recent works show that …

Deep neural networks can generate images that are astonishingly realistic, so much so that
it is often hard for humans to distinguish them from actual photos. These achievements have …

With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

In this work we ask whether it is possible to create a" universal" detector for telling apart real
images from these generated by a CNN, regardless of architecture or dataset used. To test …

In the last few years, we have witnessed the rise of a series of deep learning methods to
generate synthetic images that look extremely realistic. These techniques prove useful in the …

Generative adversarial networks (GANs) have emerged as a powerful unsupervised method
to model the statistical patterns of real-world data sets, such as natural images. These …

Understanding the capability of Generative Adversarial Networks (GANs) in learning the full
spectrum of spatial frequencies, that is, beyond the low-frequency dominant spectrum of …

Generative Adversarial Networks (GANs) have been shown to produce realistically looking
synthetic images with remarkable success, yet their performance seems less impressive …