Pixel-scale counting-based morphological pattern spectrum for the JPEG binary image manipulation detection

Recently, the digital images are used everywhere. The digital images are need at an investigation, court case, our daily life, and so on. However, the digital images are easily edited by anyone. Therefore, the digital image is required to have authenticity. The morphological pattern spectrum has been proposed as a novel technology to detect manipulation. This proposed technology was used a total pixel values approach, which can detect manipulation from the change of the pixel values in an image. This approach can detect the manipulated image and also judge the rotated image without manipulation from the original image. Therefore, this approach can detect manipulation with high accuracy. However, this approach has a problem not to judge a compressed image like JPEG format because the compressed image is changed the pixel values from the original image. The morphological pattern spectrum used a pixel-scale counting approach is proposed as novel technology to improve this problem. This improved approach is counting the number of same pixel scale as a structuring element size. It is implemented in Python for the purpose of being embedded on mobile devices and using with AI technology in the future. Therefore, this improved approach can detect the compressed image because it isn't affected by the change of the brightness values. In this paper, this improved approach is verified to judge the original image and the JPEG image with and without manipulation from the original image. In addition, the pixel-scale counting-based morphological pattern spectrum and the famous existing technology are compared. From these results, the pixel-scale counting approach based morphological pattern spectrum can detect manipulation from the JPEG image, and is confirmed superiority in detection accuracy, detection capability understandable for human eye, supports a variety of image formats, and usability than the existing technology.