With the rapid progress in recent years, techniques that generate and manipulate
multimedia content can now provide a very advanced level of realism. The boundary …

Seeing is not believing anymore. Different techniques have brought to our fingertips the
ability to modify an image. As the difficulty of using such techniques decreases, lowering the …

Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …

As manipulating images by copy-move, splicing and/or inpainting may lead to
misinterpretation of the visual content, detecting these sorts of manipulations is crucial for …

The key challenge of image manipulation detection is how to learn generalizable features
that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on …

As advanced image manipulation techniques emerge, detecting the manipulation becomes
increasingly important. Despite the success of recent learning-based approaches for image …

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

Image manipulation detection has attracted considerable attention owing to the increasing
security risks posed by fake images. Previous studies have proven that tampering traces …

Since image editing methods in real world scenarios cannot be exhausted, generalization is
a core challenge for image manipulation detection, which could be severely weakened by …

Image manipulation detection (IMD) is of vital importance as faking images and spreading
misinformation can be malicious and harm our daily life. IMD is the core technique to solve …