## 5 Conclusions  
This paper presents the most significant methods published in recent years for verifying the integrity of digital images in the context of increasingly advanced manipulation techniques. Both passive and active approaches were reviewed to achieve this goal, highlighting their main advantages, limitations, and practical applications. This paper discusses the development trends in neural network structures and architecture, ranging from traditional convolutional models through attention modules to innovative solutions based on transparent watermarking. This provides a better estimation of trends in forgery detection, aligning with this study's primary objectives. A notable contribution is the detailed presentation of the most popular datasets, aiding researchers in quickly selecting the optimal set for their needs. Additionally, the limitations of individual image collections and the types of data they contain are outlined. One of the current challenges is creating a reliable testing environment that enables accurate and meaningful performance measurement of anomaly detection algorithms under conditions resembling real-world scenarios. Although datasets such as DEFACTO are available, their scale, data quality, and annotations do not always keep pace with contemporary, sophisticated image manipulation techniques. Therefore, collaboration \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Name** & **Dataset** & **Metric** & **Value** & **Comments** \\ \hline \multirow{4}{*}{ManTra-Net [43]} & CASIA & F1 Score & - & \\  & & AUC & 81.7\% & \\  & NIST16 & F1 Score & - & \\  & & AUC & 79.5\% & used CASIA v1.0 \\  & COVERAGE & F1 Score & - & \\  & & AUC & 81.9\% & \\ \hline \multirow{4}{*}{SPAN [46]} & CASIA & F1 Score & 38.2\% & \\  & CASIA & AUC & 79.72\% & \\  & NIST16 & F1 Score & 58.2\% & some data from the NIST16 test set were \\  & & AUC & 83.95\% & also present in the training set \\  & COVERAGE & F1 Score & 55.8\% & \\  & & AUC & 92.22\% & \\ \hline \multirow{4}{*}{ObjectFormer [52]} & CASIA & F1 Score & 57.9\% & \\  & & AUC & 88.2\% & \\  & NIST16 & F1 Score & 82.4\% & \\  & & AUC & 99.6\% & \\  & COVERAGE & F1 Score & 75.8\% & \\  & & AUC & 95.7\% & \\ \hline \multirow{4}{*}{MSCL-Net [19]} & CASIA & F1 Score & 85.9\% & \\  & & AUC & 86.2\% & \\ \cline{1-1}  & NIST16 & F1 Score & 82.1\% & used both CASIA v1 and CASIA v2 \\ \cline{1-1}  & & AUC & 84.6\% & \\ \cline{1-1}  & COVERAGE & F1 Score & 71.9\% & \\ \cline{1-1} \cline{2-5}  & MS-COCO & PSNR & 38.05 dB & to obtain an unambiguous F1 metric and \\ \cline{1-1}  & & SSIM & 0.94 & precision, the results were averaged for \\ \cline{1-1}  & & F1 Score & 78.8\% & three types of attacks: copy–move, \\ \cline{1-1}  & & Precision & 80.1\% & inpainting, and splice. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics of methods on various datasets. between institutions responsible for creating multimedia content is crucial to developing further high-quality reference datasets for evaluating forgery detection systems. Another somewhat overlooked aspect is the model's efficiency and the conditions under which they are deployed. For example, many publications lack references to processing time and hardware requirements, which can hinder the practical implementation of a method. Additionally, some models operate at low resolutions (e.g., 200 px), and the downsampling process can result in significant information loss. Equally important is the analysis of video materials, which must account for spatiotemporal aspects and annotations' high costs and complexity. In the future, special emphasis should be placed on designing solutions resilient to automated manipulations generated by advanced AI models and integrating various approaches into cohesive detection systems--tailored to specific needs and use cases. Ensuring the availability of these technologies on mobile platforms and embedded systems is crucial for widespread adoption and protecting the integrity of information.