## 3 Image Manipulation Datasets
The dataset contains 105,000 images with this type of manipulation. * Copy-move (within the same image): Elements within a single image are duplicated and placed elsewhere within the same image. To maintain realism, the position of copied objects is controlled along specific axes (vertical or horizontal) depending on the object's dimensions. Alpha matting techniques are used to refine the edges of the objects. The dataset contains 19,000 images with this type of manipulation. * Removing objects (inpainting): This involves removing objects from images using inpainting techniques. This method fills empty areas by synthesizing the background based on surrounding pixels, allowing for smooth and natural image completion. Objects selected for removal are typically located against relatively uniform backgrounds. The dataset contains 19,000 images with this type of manipulation. * Morphing (face morphing): Two images are deformed and merged to create a single image combining features of both sources. For facial images, this includes face blending and swapping. Facial landmarks are detected using the Dlib library [29], enabling the precise alignment and merging of faces. Additionally, color-matching techniques are applied to ensure consistent skin tone and lighting between the combined images. The dataset contains 80,000 images with this type of manipulation. To generate these forgeries, the DEFACTO dataset uses the MSCOCO dataset [30] as a source of images and initial object annotations. However, the raw segmentation masks from MSCOCO are not precise enough for high-quality manipulations. Therefore, the authors applied alpha matting techniques to refine these masks, resulting in improved object edges and eliminating obvious manipulation artifacts, such as sharp edges or inconsistent lighting. For each image in this dataset, a binary ground truth mask and metadata in the form of a JSON file describing the image transformation process are available. The Columbia Image Splicing Detection Evaluation Dataset [31] is one of the first and most well-known datasets used in research on detecting splicing manipulations in digital images. Developed by the DVMM Laboratory at Columbia University, this dataset was created to support the development of passive forgery detection techniques that do not require additional information about the image or metadata. The dataset consists of 1845 images, all of a fixed size of 128 \(\times\) 128 pixels. These images were extracted from larger photographs available in the CalPhotos collection [32], as well as from a small number of photos taken with digital cameras. The dataset contains a comparable number of original and manipulated images. The manipulations primarily involve splicing, and the data are categorized based on specific characteristics:  
* Smooth vs. textured: Images with uniform surfaces compared to those with complex textures. * Arbitrary object boundaries vs. simple boundaries: Manipulations with irregular, complex edges compared to straight dividing lines. One limitation of this dataset is the lack of pixel-level ground truth masks. IMD2020 [33; 34] is a comprehensive dataset developed by [PERSON] and collaborators. It consists of several subsets encompassing diverse types of manipulations and a wide range of authentic images, enabling thorough testing of detection methods under realistic conditions. The first component of the dataset is IMD2020 Real-Life Manipulated Images, containing 2010 forged images sourced from the Internet. Each manipulated image has a corresponding original version, allowing precise comparison and analysis of the manipulations. Additionally, manually created binary masks are provided for each forged image, localizing the manipulated areas at the pixel level. Another component is the IMD2020 Large-Scale Set of Real Images, comprising 35,000 authentic (non-manipulated) images collected from 2322 camera models. All images were manually reviewed, and those showing visible traces of digital manipulation were excluded.