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 …

Image is known as one of the communication tools between humans. With the development
and availability of digital devices such as cameras and cell phones, taking images has …

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 …

In this paper we present TruFor, a forensic framework that can be applied to a large variety
of image manipulation methods, from classic cheapfakes to more recent manipulations …

Recent advances in image editing techniques have posed serious challenges to the
trustworthiness of multimedia data, which drives the research of image tampering detection …

To fight against real-life image forgery, which commonly involves different types and
combined manipulations, we propose a unified deep neural architecture called ManTra-Net …

In the era of AI-generated content (AIGC) malicious tampering poses imminent threats to
copyright integrity and information security. Current deep image watermarking while widely …

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 …

Image manipulation detection is different from traditional semantic object detection because
it pays more attention to tampering artifacts than to image content, which suggests that richer …