Multimedia forensics is increasingly becoming an important research area for protecting public safety and enhancing national security; steganalysis and forgery detection are becoming two active research areas, even as they are still in their inception. The shortage of comprehensive detection systems has severely impeded progress and practical applications in the field. This study will facilitate the work of forensic practitioners in criminal investigations and will deliver to the community benchmark datasets, performance evaluation standards and models, software design and the analysis toolkit in order to design and test new steganalysis/forgery detection system approaches. Additionally, as part of the research education plan, undergraduate women, minorities, and first-generation college students will be extensively involved. A further contribution to an educational pipeline for computer science involves local high school students through the creation of a multimedia forensics and knowledge discovery laboratory.
The research involves designing comprehensive hybrid intelligent systems for steganalysis and forgery detection with the capability to discriminate the operations of steganography, general processing and forgery manipulation. This research will make contributions to engineering by providing the optimal classification/regression models associated with the identification of minimal optimal feature sets, benefitting a broad range of applications. The scientific objectives of the research involve validation of new paradigms, shift-recompression-based derivative/wavelet differential pattern analyses on multimedia data, to merge high dimensional rich models that were used for steganalysis, and apply judicious feature mining and machine learning techniques to handle high dimensionality and establish comprehensive hybrid intelligent forensics systems to recognize different categories of multimedia data and reveal past processing history.
