CIF: SMALL: RUI: Novel Detection Approaches with Comprehensive Hybrid Intelligent Systems for Multimedia Forensics

多媒体取证正日益成为保护公共安全和增强国家安全的重要研究领域;隐写分析和伪造检测正成为两个活跃的研究领域，尽管它们仍处于起步阶段。缺乏全面的检测系统严重阻碍了这一领域的进展和实际应用。这项研究将促进法医从业人员在刑事调查中的工作，并将向社区提供基准数据集、性能评估标准和模型、软件设计和分析工具包，以设计和测试新的隐写分析/伪造检测系统方法。此外，作为研究教育计划的一部分，女大学生、少数民族和第一代大学生将广泛参与。通过建立多媒体取证和知识发现实验室，对计算机科学教育管道的进一步贡献涉及当地高中学生。该研究涉及设计全面的混合智能系统隐写分析和伪造检测的能力，以区分隐写术，一般处理和伪造操作。本研究将通过提供与最小最优特征集识别相关的最优分类/回归模型，为工程做出贡献，受益于广泛的应用。该研究的科学目标包括验证新的范式，基于移位再压缩的导数/小波差分模式分析多媒体数据，合并用于隐写分析的高维丰富模型，并应用明智的特征挖掘和机器学习技术来处理高维数据，建立综合的混合智能取证系统，以识别不同类别的多媒体数据并揭示过去的处理过程历史

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.