Congratulations to Ron Salloum for Passing His Defense
Congratulations to Ron Salloum for passing his PhD defense on April 10, 2019. His PhD thesis is entitled “A Data-Driven Approach to Image Splicing Localization”.
Abstract:
The availability of low-cost and user-friendly editing software has made it significantly easier to manipulate images. Thus, there has been an increasing interest in developing forensic techniques to detect and localize image manipulations or forgeries. Splicing, which is one of the most common types of image forgery, involves copying a region from one image (referred to as the donor image) and pasting it onto another image (referred to as the host image). Forgers often use splicing to give a false impression that there is an additional object present in the image, or to remove an object from the image.
Many of the current splicing detection methods only determine whether a given image has been spliced and do not attempt to localize the spliced region. Relatively few methods attempt to tackle the splicing localization problem, which refers to the problem of determining which pixels in an image have been manipulated as a result of a splicing operation.
In my dissertation, I present two different splicing localization methods that we have developed. The first is the Multi-task Fully Convolutional Network (MFCN), which is a neural-network-based method that outperforms previous methods on many datasets. The second proposed method is based on cPCA++ (where cPCA stands for contrastive Principal Component Analysis), which is a new data visualization and clustering technique that we have developed. The cPCA++ method is more efficient than the MFCN and achieves comparable performance.
PhD Experience:
Pursuing my PhD degree was a very challenging but rewarding experience. I really enjoyed my time in the Media Communications Laboratory and had the opportunity to work on exciting research projects. [...]