Digital image authenticity has promoted research on image forgery localization. Although
deep learning-based methods achieve remarkable progress, most of them usually suffer …

Face swap** has drawn a lot of attention for its compelling performance. However, current
deepfake methods suffer the effects of obscure workflow and poor performance. To solve …

Digital images have become an important carrier for people to access information in the
information age. However, with the development of this technology, digital images have …

Multimedia images have become an important way for the sharing of digital information.
However, advanced editing tools provide easy methods for malicious content modification …

Recent advancements in language-image models have led to the development of highly
realistic images that can be generated from textual descriptions. However, the increased …

Digital image forensics plays a crucial role in image authentication and manipulation
localization. Despite the progress powered by deep neural networks, existing forgery …

Modern image editing software enables anyone to alter the content of an image to deceive
the public, which can pose a security hazard to personal privacy and public safety. The …

Abstract Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against
presentation attacks. With advancements in sensor manufacture and multi-modal learning …

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 this work, borrowing a solution from the large-scale vision-language models (VLMs)
instead of directly removing modality-specific signals from visual features, we propose a …