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 …

We are currently on a verge of a revolution in digital photography. Developments in
computational imaging and adoption of artificial intelligence have spawned new editing …

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 …

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 …

The diffusion of fake images and videos on social networks is a fast growing problem.
Commercial media editing tools allow anyone to remove, add, or clone people and objects …

We introduce a novel deep neural architecture for image copy-move forgery detection
(CMFD), code-named BusterNet. Unlike previous eorts, BusterNet is a pure, end-to-end …

In this work, we propose a technique that utilizes a fully convolutional network (FCN) to
localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on …

Detecting and localizing image splicing has become essential to fight against malicious
forgery. A major challenge to localize spliced areas is to discriminate between authentic and …

The emergence of powerful image editing software has substantially facilitated digital image
tampering, leading to many security issues. Hence, it is urgent to identify tampered images …

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