Andy Zhu

Machine learning algorithms for reverse image search (a subset of open source intelligence or OSINT)provide a free, useful tool for determining the content of an image, where and when it was captured, and in some cases whether it has been digitally modified. Using a test data set of 24 images, we compared the performance of reverse image search for Google, Bing, and Yandex. Our black box experimental results are presented for three different categories of images (uncluttered images, images with significant background clutter, and people recognition). We compare these results with previous studies and review how relative performance has changed over time. We validate our results using EXIF data and ELA analysis of selected images. Based on these results, best practices for OSINT image investigation are proposed.