# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 1 Introduction  
In an era of the widespread circulation of digital visual content, both in social media and professional industry environments, ensuring the integrity and credibility of images has become a significant challenge. The rapid advancement in image editing tools, including algorithms based on machine learning and artificial intelligence [1], makes photo manipulations [2] ranging from subtle retouching to complex alterations, increasingly difficult to detect. Consequently, there is an urgent need to develop methods that not only verify the authenticity of visual materials but also identify areas where potential modifications have occurred. The development of both passive and active manipulation detection methods aims to counteract forgeries and facilitate the verification of file authenticity. Passive approaches rely on analyzing image statistics to detect characteristic artifacts without prior intervention in the content; however, their effectiveness is limited, and they do not guarantee the detection of all unauthorized modifications [3]. In contrast, active methods, including the one presented in this study, involve embedding a special, hard-to-remove signature (e.g., watermarks) into the image during its creation or processing [4]. Moreover, many advanced data-hiding methods utilize modern deep learning techniques for video identification [5, 6], images [7] and even network protocol consistency [8, 9, 10, 11]. Each of these methods confirms the paradigm of maintaining perceptual and steganalytic transparency. Inspired by the concept of watermarks and steganographic methods based on deep learning [12, 13], we propose an active approach to image integrity protection using watermarking techniques. Unlike traditional steganographic or watermarking solutions, where the primary goal is to conceal additional information (e.g., an identifier or a secret message) within the image structure, our method aims to develop a \"hidden signature\" that does not serve as a mere data carrier but rather as a global pattern enabling the direct analysis and localization of manipulated areas. A key element of our approach is using unsupervised training processes, which ensures the high transparency of the embedded pattern while avoiding the limitations related to extracting a specific marker or character sequence. Instead of treating the image as a transmission medium for hidden information, this method focuses on directly estimating manipulation fields on the recipient's side and performing the binary detection of image interference. Leveraging this principle allows for a significant simplification and acceleration of post-reception processing steps. As demonstrated in later sections of this paper, our method enables efficient scaling to very high resolutions, facilitating the simple and rapid marking of visual data. To the best of the authors' knowledge, this type of solution has not yet been published. It represents a significant advancement, opening new possibilities for practical applications of active image manipulation protection methods. In summary, the proposed solution introduces several key elements to the current state of knowledge, which can significantly impact the development of active image protection methods:  
* Full flexibility--enables embedding integrity markers in images of virtually any resolution and aspect ratio without additional model retraining;
* High transparency--achieving an SSIM > 0.999 and PSNR > 60 dB ensures an almost imperceptible interference with the original image (especially for high/native resolutions);
* A simple yet effective model--even a standard UNet architecture, enhanced with a classification module, achieves excellent results in embedding and detecting signatures thanks to the original data flow.