## 4 Methods for Manipulation Protection and Detection
### Summary  
The summary of the methods discussed in this section is presented below--Tables 2 and 3.  
We are witnessing the rapid development of image manipulation detection methods based on deep learning, driven by increasing threats associated with the ease of editing and generating multimedia content using advanced generative models [9; 14; 20]. Current deep learning methods [62; 63], such as ManTra-Net, SPAN, and MSCL-Net, demonstrate significant improvements in manipulation detection accuracy, achieving progressively higher metrics such as F1 Score and AUC on standard datasets. The importance of statistical analysis and frequency-domain features in detecting inconsistencies in images is emphasized [64; 65]. Trends indicate a growing interest in passive methods, which, by leveraging the latest advancements in deep learning such as self-attention mechanisms, contrastive learning, and hybrid architectures [66], effectively identify subtle inconsistencies in image structures.  
Active methods, such as those proposed by [PERSON] et al. [35] and [PERSON] et al. [36], are gaining importance, despite requiring additional effort during the image generation process. Techniques based on watermarking (or other invisible markers) significantly enhance effectiveness in both detecting modified areas and overall manipulation detection.  
Furthermore, watermarks allow for the transmission of a small data payload, which can include information about the source, authenticity, or context of the image, providing an additional layer of protection. Solutions in this category offer potential benefits for applications demanding a high level of data integrity.  
When comparing both strategies, passive methods are more versatile as they do not require prior embedding of data into the image. However, they may be less effective against advanced manipulations generated by diffusion models or GANs. On the other hand, active methods offer higher security, but their effectiveness depends on controlling the image creation and distribution process. Moving forward, the focus will be on developing models capable of generalizing to new types of manipulation algorithms and resistant to attacks by  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method Name** & \begin{tabular}{c} **Publication** \\ **Year** \\ \end{tabular} & **Method Type** & **Output Type** & **Key Features** & \begin{tabular}{c} **Datasets Used** \\ **in Evaluation** \\ \end{tabular} \\ \hline ManTra-Net [43] & 2019 & Passive & Localization & \begin{tabular}{c} End-to-end CNN; detects \\  & & & 385 types \\  & & & of manipulations & CoMoFoD \\ \hline SPAN [46] & 2020 & Passive & Localization & \begin{tabular}{c} Self-attention mechanisms; \\ better spatial \\ relation interpretation \\ \end{tabular} & NIST16, CASIA \\ \hline [PERSON] et al. [35] & 2022 & Active & Detection & \begin{tabular}{c} Adding pre-trained \\ templates to images \\ \end{tabular} & Own dataset \\ \hline ObjectFormer [52] & 2022 & Passive & \begin{tabular}{c} Detection and \\ Localization \\ \end{tabular} & \begin{tabular}{c} Multimodal approach; \\ architecture based \\ on transformers \\ \end{tabular} & CASIA, Coverage, NIST16 \\ \hline [PERSON] et al. [36] & 2023 & Active & Detection & \begin{tabular}{c} Deep semi-fragile \\ watermark; use of \\ UNet architecture \\ \end{tabular} & Own dataset \\ \hline MSCL-Net [19] & 2024 & Passive & \
\begin{tabular}{c} Detection and \\ Localization \\ \end{tabular} &
\begin{tabular}{c} Multi-scale contrastive \\ learning; \\ attention mechanisms \\ \end{tabular} & CASIA, Coverage, NIST16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of key features of the discussed methods.  
generative models, where no original image is available for comparison. Researchers will emphasize creating more advanced neural network architectures that leverage emerging innovations in deep learning and multimodal analysis to counter increasingly sophisticated manipulation techniques effectively.