# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 4 Conclusions  
The 4 KSecure solution presented in this paper demonstrates an effective method for actively embedding integrity markers in high-resolution images, enabling manipulation detection throughthe binary classification and precise identification of altered regions within the image structure. Throughout the study, it was observed that the proposed method has a minimal impact on visual quality, as confirmed by the obtained SSIM, PSNR and LPIPS metric results. Additionally, its flexible architecture allows its adaptation to images of nearly any aspect ratio and size, it being successfully tested on formats reaching up to 4K (3840 \(\times\) 2160). A key concept of this approach is the \"mixing\" of memory between the system's CPU and the accelerator (GPU), enabling the efficient processing of very large images without the need to scale them down significantly. However, this approach also introduces specific challenges. First, the lack of standardized data sizes (as seen in models trained on ImageNet, where images are typically resized to 256 \(\times\) 256 pixels) can lead to unstable RAM usage. From an implementation perspective, this requires careful resource management, particularly when handling diverse and non-standard input formats. Second, larger images result in higher computational costs during training and inference, which poses a challenge for practical deployments, especially in edge computing environments with limited processing power. Furthermore, the algorithm requires the prior embedding of integrity markers, meaning that the creation or modification of visual content must be controlled; otherwise, the method cannot be applied. Despite these limitations, the results clearly show that 4 KSecure offers a significant advantage over other approaches in terms of a high manipulation-detection accuracy and the preservation of image quality. The integration of the UNet architecture, enhanced with an additional classification module in the decoder and the interpolation mechanism, yields promising results. The proposed pipeline can be a foundation for further optimization, particularly by exploring additional compression techniques or segmenting the image into subregions to further balance computational resource demands. Moreover, extending the method to \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{[PERSON] et al. [59]} & \multirow{2}{*}{2024} & \multirow{2}{*}{COCO} & Potentially any & & & & The method can only work at the specific \\  & & & commercial size & & & & & resolution on which it \\  & & & (tested up to & & & & & was trained. The \\  & & & 1920 \(\times\) 1080) & & & & & authors achieved an \\  & & & & & & & & accuracy of 99.90\%. \\  & & & & & & & & A separate and \\  & & & & & & & & coordinated \\  & & & & & & & & watermark for the \\  & & & & & & & video track and audio, \\ V\({}^{2}\)A-Mark [60] & 2024 & Vimeo-90K [61] & 448 \(\times\) 256 & 0.972 & 0.983 & 40.83 & 40.83 & \\  & & & DAVIS [62] & & & & & & \\  & & & & & & & & key can combine what \\  & & & & & & & & is decoded from both \\  & & & & & & & & the image and the \\  & & & & & & & sound. \\ [PERSON] et al. [31] & 2024 & FFHQ & 256 \(\times\) 256 & 0.95 & 0.94 & 38.05 & \\  & & CelebA & & Potentially any & & & & \\  & & & & commercial size & & & & \\ (high-resolution) & 2025 & Our dataset & (tested up to & 0.9668 & 0.9996 & 60.2165 & \\  & & & & 3840 \(\times\) 2160) & & & & (1936 \(\times\) 2592) is the \\  & & & & & & & & resolution in the \\ Ours & 2025 & ImageNet-1K & & & & & & ImageNet-1K dataset. \\  & & & & & & & & \\ (high-resolution) & 2025 & ImageNet-1K & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._video sequence analysis, incorporating a temporal dimension, represents a natural direction for development in response to the growing popularity of video content. In conclusion, 4 KSecure can be seen as a step forward in developing active methods for ensuring image authenticity.