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 …

We consider the generic problem of detecting low-level structures in images, which includes
segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow …

To defend against manipulation of image content, such as splicing, copy-move, and
removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to …

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 …

The spread of misinformation through synthetically generated yet realistic images and
videos has become a significant problem, calling for robust manipulation detection methods …

We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and
localization of multiple types of image manipulations. The proposed architecture efficiently …

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

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 …

Witnessing impressive results of deep nets in a number of computer vision problems, the
image forensic community has begun to utilize them in the challenging domain of detecting …