Document Image Forgery Detection Using RGB Color Channel - Gornale, Shivanand S.; Patil, Gayatri; Benne, Rajkumar (Transactions on Engineering and Computing Sciences, 2022)
Using advanced digital technologies and photo editing software, document images, such as typed and handwritten documents, can be manipulated in a variety of ways. The most common method of document forgery is adding or removing information. As a result of the changes made to document images, there is misinformation and misbelief in document images. Forgery detection with multiple forgery operations is challenging issue. As a result, special consideration is given in this work to the ten-class problem, in which a text can be altered using multiple forgery types. The characteristics are computed using RGB color components and GLCM texture descriptors. The method is effective for distinguishing between genuine and forged document images. A classification rate of 95.8% for forged handwritten documents and 93.11% for forged printed document images are obtained respectively. The obtained results are promising and competitive with state-of- art techniques reported in the literature.