# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 2 Related Works
### Deep Learning in Ensuring Image Integration  
Since 2015, there has been a noticeable surge in interest in deep neural networks for image manipulation detection and localization. Many earlier studies relied on analyzing noise patterns [23] or color consistency [24]. Today, the field has shifted mainly towards deep learning-based methods and the analysis of deep features extracted using neural networks, as evidenced by numerous scientific publications. [PERSON] et al. published one of the first studies to utilize neural networks for image manipulation classification [25]. This work proposed using CNNs to detect median filtering as a form of image manipulation. The network produced a binary classification output, determining whether an image had been manipulated. A year later, [PERSON] and [PERSON] [26] proposed extending this method by using CNNs to classify five different types of manipulations. A further important step came with the introduction of ManTra-Net in 2019 [27]. ManTra-Net was one of the first end-to-end solutions based on fully convolutional neural networks. Its architecture was divided into two main components: the first, the \"image manipulation-trace feature extractor\", was responsible for generating a uniform representation of features, and the second, the \"local anomaly detection network\", focused on anomaly localization. This structure directly detected manipulated areas in an image, eliminating the need for additional processing. The method could distinguish various known manipulations, including operations generated by other neural networks, such as image inpainting (filling in missing parts of an image).  
A year later, in 2020, SPAN (Spatial Pyramid Attention Network) [28] was introduced to improve manipulation localization by effectively capturing dependencies between points and objects at different scales using self-attention mechanisms [29]. SPAN offered more precise manipulation detection than ManTra-Net, which primarily focused on analyzing the same image points across different feature map scales. SPAN models both scale dependencies through multi-scale information propagation and use spatial dependencies between different image regions using a self-attention module. In recent years, an interesting approach was also presented in ObjectFormer [30]. This method stands out for its multimodal approach to manipulation detection, analyzing both the RGB and frequency domains. Thanks to this dual-domain analysis, it becomes possible to detect invisible traces of editing in the standard color spectrum. ObjectFormer's architecture is based on transformers, which model spatial dependencies within an image. In addition to passive methods, active watermark-based approaches are gaining increasing interest. These methods embed a watermark in the image as a mask, which can later be extracted to detect potential modifications. One such approach is the transparent watermark concept introduced by [PERSON] et al. [31]. A key component of this solution is using two neural network models that are responsible for generating and recovering the watermark. The watermark is a separate image that is transformed into a feature matrix and added to the original image. Then, based on the observed changes in the extracted image on the recipient's side, it is possible to predict the area that has undergone manipulation.