We review the broad variety of methods that have been proposed for anomaly detection in
images. Most methods found in the literature have in mind a particular application. Yet we …

Concernings related to image security have increased in the last years. One of the main
reasons relies on the replacement of conventional photography to digital images, once the …

Learning to detect automatic anomalies in production plants remains a machine learning
challenge. Since anomalies by definition cannot be learned, their detection must rely on a …

In a world teeming with digital images, the credibility of visual data has become of
paramount importance. While it is now simpler than ever to manipulate an image for …

Denoising, the process of reducing random fluctuations in a signal to emphasize essential
patterns, has been a fundamental problem of interest since the dawn of modern scientific …

This work proposes a novel method for detecting JPEG compression, as well as its grid
origin, based on counting the number of zeros in the DCT of 8× 8 blocks. When applied …

In this work, we propose a one-class self-supervised method for anomaly segmentation in
images that benefits from both a modern machine learning approach and a more classic …

In this paper a semi-supervised method for the detection of anomalies in both texture-and
object-based product images is presented. The method exploits a pre-trained Convolutional …

Purpose The purpose of this paper is to present a novel method for minor fabric defects
detection. Design/methodology/approach This paper proposes a PETM-CNN algorithm …

Anomaly detectors address the difficult problem of detecting automatically exceptions in a
background image, that can be as diverse as a fabric or a mammography. Detection …