## 3 Image Manipulation Datasets  
The effectiveness of image manipulation detection methods largely depends on the quality and diversity of datasets used for training and testing models [21]. In recent years, several specialized datasets containing both original and manipulated images have been developed, enabling researchers to compare various approaches and detection techniques [22]. In this section, we provide an overview of the most important datasets used in the field of image manipulation detection, discussing their characteristics, applications, and availability. CoMoFoD (Copy-Move Forgery Detection) [23] is a dataset developed by [PERSON] et al., in 2013, designed for studying methods of detecting copy-move manipulations in digital images. The dataset consists of two groups of images divided by size: small images with dimensions of 512 \(\times\) 512 pixels and large images with dimensions of 3000 \(\times\) 2000 pixels. In the small image category, there are 10,000 examples, of which 5000 are original images and 5000 are images containing forgeries created using the copy-move method. In the large image category, there are 3000 examples, split into 1500 originals and 1500 manipulated images. The forgery creation process included various geometric transformations, such as translation, rotation, scaling, distortions, and their combinations, which enhances the diversity and realism of manipulations. Additionally, all images--both original and manipulated-- underwent a series of post-processing operations. These include JPEG compression with different quality factors (ranging from 20 to 90), Gaussian blur with various parameters, noise addition using median filters of different sizes, brightness adjustments, color reduction, and contrast regulation. This extensive range of post-processing operations allows testing the robustness of detection algorithms against various distortions and transformations that may occur in practice. The advantage of the CoMoFoD dataset is its diversity and complexity. The availability of ground truth masks enables the precise evaluation of manipulation detection methods at the pixel level. However, the disadvantage is the lack of information about the exact parameters of the applied geometric transformations. CASIA [24] is one of the most significant public datasets used in research on detecting manipulations in digital images, particularly for operations such as splicing or copying parts of an image. This dataset was developed by the Chinese Academy of Sciences and is available in two versions: CASIA V1.0 and CASIA V2.0. * CASIA V1.0 is the original version of the dataset, containing images of relatively low resolution. It includes 800 original images and 921 manipulated images. The uniform sizes of the images in this version facilitate processing and analysis. * CASIA V2.0 is an extended version of the dataset, significantly larger and more diverse in terms of image resolution. It contains 7491 original images and 5123 manipulated images. The resolutions of the images in this version are varied--see Table 1.  
Manipulations introduced in the CASIA dataset include various techniques such as splicing and copying and pasting elements within the same image. To increase the realism of the forgeries and make detection more challenging, additional post-processing operations were applied to the manipulated areas, such as blurring, contrast adjustment, and noise addition. A disadvantage of the CASIA dataset is the lack of ground truth masks for manipulated images, which makes it difficult to accurately evaluate the effectiveness of detection methods at the level of change localization. Researchers must primarily rely on classification metrics that assess whether an image is original or manipulated, without the capability to identify the areas of intervention precisely. Despite this limitation, CASIA serves as a benchmark in numerous scientific studies.