Publication Type : Conference Paper
Thematic Areas : Humanitarian-Robotics-HCI
Publisher : International Conference on Advancements in Automation Robotics and Sensing.
Source : International Conference on Advancements in Automation Robotics and Sensing (ICAARS), India, 2016.
Campus : Amritapuri
School : School of Engineering, Center for Gender Equality and Women Empowerment, Department of Computer Science and Engineering, Department of Social Work
Center : Ammachi labs, Center for Gender Equality and Women Empowerment
Department : Computer Science, Social Work
Verified : Yes
Year : 2016
Abstract : Copy–Move Forgery Detection (CMFD) helps to detect copied and pasted areas in one image. It plays a crucial role in legal evidence, forensic investigation, defence, and many more places. In the proposed CMFD method, a two-step identification of forgery is presented. In step one, the suspected image will be classified into either one of two classes that are forged or authentic. Step two is carried out only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure. Initially, the suspected image is decomposed into different orientations using Steerable Pyramid Transform (SPT); Grey Level Co-occurrence Matrix (GLCM) features are extracted from each orientation. These features are used to train Optimized Support Vector Machine (OSVM) as well as to classify. If the suspected image is categorized into forged, then the suspected grey image is converted into overlapping blocks, and from each block, GLCM features are extracted. The proper similarity threshold value and distance threshold value can locate the forged region using GLCM block features. The performance of the proposed method is tested using standard datasets CoMoFoD and CASIA Datasets. The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation. The OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).
Cite this Research Publication : Maya Menon, Udupa, G., Nair, G. J., and Rao R. Bhavani, “Localization and Mapping Using Statistical Image Processing Methods”, in International Conference on Advancements in Automation Robotics and Sensing (ICAARS), India, 2016.