Generative deep learning techniques have invaded the public discourse recently. Despite
the advantages, the applications to disinformation are concerning as the counter-measures …

This study introduces a new method for detecting and localizing image forgery by focusing
on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB …

In the face of increasingly realistic fake human faces, research on enhancing the differences
between real and fake images is valuable for improving the generalization capabilities of …

Although deepfake technology is neutral, it can be maliciously used by criminals to cause
serious security issues. These deepfake videos generated by deep learning technology are …

Recent advances in document forgery techniques produce malicious yet nearly visually
untraceable alterations, imposing a big challenge for document tampering localization …

Image forgery localization identifies tampered regions within an image by extracting
distinctive forgery features. Current methods mainly use convolutional neural networks …

In recent years, the rapid technological development and the emergence of mobile devices,
cameras, etc., in addition to the availability of video production, editing, and formatting …