## 3 Image Manipulation Datasets
MICC (Media Integration and Communication Center) [25] is one of the earliest and most renowned datasets used in research on detecting manipulations in digital images, particularly for methods involving copy-paste operations. Developed by a team from the University of Florence, this dataset consists of several subsets: MICC-F220, MICC-F2000, MICC-F600, and MICC-F8 multi. * MICC-F220 contains 220 high-resolution images, half of which are original and half manipulated using copy-paste techniques. These manipulations often include additional geometric transformations, such as rotation or scaling, increasing the difficulty of detection. * MICC-F2000 is an extended dataset consisting of 2000 images, including 1300 original and 700 manipulated. * MICC-F600 includes 600 high-resolution images featuring diverse scenes and objects. As in the previous subsets, the manipulations involve copying and pasting parts of the image, with 440 original images and 160 manipulated ones. * MICC-F8 multi is a small subset comprising eight images with multiple manipulations. This subset allows for testing algorithms in scenarios where a single image contains several independent forgeries. A drawback of this dataset (similar to the CASIA dataset) is the lack of ground truth masks that would indicate the precise manipulated regions at the pixel level. NIST16 [26] is a dataset created in 2016 by the National Institute of Standards and Technology (NIST) to support research on detecting manipulations in digital images. This dataset contains 3032 files, including 1422 test images (probe) in jpg format, with the remainder consisting of reference images, ground truth masks, and metadata files. The dataset is characterized by high-resolution images, ranging from 360 \(\times\) 480 to 4032 \(\times\) 3024 pixels. The dataset is divided into three main manipulation categories: manipulation, removal, and splice. Each category represents a different type of forgery:  
* Manipulation includes images with subtle changes, such as modifications to specific parts of the photo or alterations aimed at enhancing its appearance. This category includes 143 ground truth masks that indicate the edited areas at the pixel level. * Removal contains images where certain elements have been removed. This group includes 101 ground truth masks. * Splice involves images created by merging fragments from different sources, resulting in realistic but fabricated compositions. This category includes 146 ground truth masks. In addition to manipulated images, the dataset includes 1244 original images, serving as a baseline for comparisons with the modified images. Metadata files are also available, specifying the transformations applied to the images, although they lack detailed transformation parameters. COVERAGE [27] is a dataset designed for research on detecting copy-paste manipulations in digital images. Created by a team of researchers from Jiao Tong University in Shanghai, it includes high-quality images that enable testing the effectiveness of various forgery detection algorithms. The dataset consists of 100 images, of which 50 are original, and the remaining 50 are manipulated using the copy-paste method. The manipulations involve a variety of geometric transformations, such as rotation, scaling, and perspective changes. Additionally,the images include similar but authentic objects, adding complexity for algorithms that must distinguish genuine similarities from those resulting from manipulations. The images in the dataset have a relatively high resolution of 400 \(\times\) 486 pixels. For each manipulated image, ground truth masks are available. DEFACTO is a comprehensive dataset created to support research on detecting image and face manipulations, developed by [PERSON] et al [28]. The DEFACTO dataset includes over 200,000 images, which were automatically generated to represent four main categories of image manipulation:  
* Splicing: This involves inserting an external element from one image into another, creating a composite image.