Event Details
Image Splicing Detection using Mask-RCNN
Presenter: Belal Ahmed
Supervisor:
Date: Mon, October 12, 2020
Time: 16:00:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
Zoom link: https://zoom.us/j/92337056968?pwd=S1NMb3ljYWdRcmZVVThWbnlJUlJTUT09
Meeting ID: 923 3705 6968
Passcode: sGt6TK
Summary:
Digital images have become a dominant source of information and means of communication in our society. However, they can easily be altered using readily available image editing tools. In this paper, we propose a new blind image forgery detection technique which employs a new backbone architecture for deep learning which is called ResNet-conv. ResNet-conv is obtained by replacing the feature pyramid network in ResNet-FPN with a set of convolutional layers. This new backbone is used to generate the initial feature map which is then to train the Mask- RCNN to generate masks for spliced regions in forged images. The proposed network is specifically designed to learn discriminative artifacts from tampered regions. Two different ResNet architectures are considered, namely ResNet-50 and ResNet-101. The ImageNet, He-normal, and Xavier-normal initialization techniques are employed and compared based on convergence. To train a robust model for this architecture, several post-processing techniques are applied to the input images. The proposed network is trained and evaluated using a computer generated image splicing dataset and found to be more efficient than other techniques.