Exploring Google Reverse Image Search to Detect Visual Plagiarism in Interior Design

Abstract

This study aims to explore the ability of Google Reverse Image Search (RIS) to detect plagiarism in images in the interior design field. Several image modifications were introduced by retaining the basic concept of the original image. These changes were classified into three categories as follow: a change in the design elements, introduced random changes by adding different objects to the existing image contents, and introduced various image effects. findings show that Google RIS does not take long to find newly uploaded images. Although it cannot detect changes related to the image contents, it can detect changes related to image size and contrast. Overall percentage of the modified images that were detected as matching the original image was only 5%. By contrast, the net percentage of images retrieved by Google RIS with contents actually related to the uploaded original image was 58.5%. Therefore, Google RIS is inaccurate in detecting any changes in the image contents irrespective of their simplicity, which implies that it cannot help in detection of visual plagiarism.