Recent advances in deep learning algorithms have shown impressive progress in image
copy-move forgery detection (CMFD). However, these algorithms lack generalizability in …

IFL (Image Forgery Location) helps secure digital media forensics. However, many methods
suffer from false detections (ie, FPs) and inaccurate boundaries. In this paper, we proposed …

In this paper, we propose a new detection and localization framework capable of detecting
suspicious forgeries using the Semantic-Agnostic Progressive Subtractive Network (SAPS …

Image copy-move forgery detection (CMFD) has become a challenging problem due to
increasingly powerful editing software that makes forged images increasingly realistic …

This research presents a new approach for identifying instances of copy-move forgeries in
digital images by utilizing the Multiscale Detector a Neural Network-based method, which …

Image copy-move forgery is a common simple tampering technique. To address issues such
as high time complexity in most copy-move forgery detection algorithms and difficulty …

With the advancement of face manipulation technology, forgery images in multi-face
scenarios are gradually becoming a more complex and realistic challenge. Despite this …

Copy-move forgery detection is a common image tampering detection technology. In this
paper, a novel copy-move forgery detection scheme is proposed. The proposed scheme is …

The widespread use of digital images on social media and the internet has led to increased
image forgery, which poses a significant challenge to their authenticity. It is crucial to …

Copy-move forgery detection aims at detecting duplicated regions in a suspected forged
image, and deep learning based copy-move forgery detection methods are in the …