With advancements in AI-generated images coming on a continuous basis, it is increasingly
difficult to distinguish traditionally-sourced images (eg, photos, artwork) from AI-generated …

Recent generative models show impressive performance in generating photographic
images. Humans can hardly distinguish such incredibly realistic-looking AI-generated …

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

With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-
synthesized images are now of such high quality that humans can hardly distinguish them …

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs)
for universal detection of AI-generated images. We develop a lightweight detection strategy …

Image manipulation dates back long before the deep learning era. The classical prevailing
approaches were based on maximizing patch similarity between the input and generated …

Anomaly detection in computer vision is the task of identifying images which deviate from a
set of normal images. A common approach is to train deep convolutional autoencoders to …

While recent advances in generative models benefit the society, the generated images can
be abused for malicious purposes, like fraud, defamation, and false news. To prevent such …

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 …

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 …