## 4 Methods for Manipulation Protection and Detection
### ManTra-Net  
One of the significant approaches within passive methods is the concept presented in the publication ManTra-Net [43], which introduced one of the first end-to-end solutions based on fully convolutional networks. The authors designed the architecture by dividing it into two main components: the first, called the \"image manipulation trace feature extractor\", which generates a unified feature representation, and the second, the \"local anomaly detection network\" (LADN), which focuses on anomaly localization. This structure directly detects tampered areas without requiring further post-processing--see Figure 2.  
According to the information provided by the authors, the developed method can distinguish up to 385 types of known manipulations, including operations generated by other neural networks, such as image inpainting. For the backbone of the architecture, three  
Figure 2: ManTra-Net high level architecture.  
types of networks were tested as follows: VGG [44], ResNet [42], and DnCNN [45]. The backbones were evaluated on the \"IMC-7\" (Image Manipulation Classification) task, which includes attacks such as compression, blurring, morphological manipulations, contrast adjustments, noise addition, resampling, and quantization. The VGG network achieved the best results, with 92.1% accuracy on the validation set, while ResNet reached 90.8% and DnCNN 91.2%. Additionally, studies were conducted to assess the impact of the type of first network layer, comparing SRMConv2D, BayarConv2D, and a classic 2D convolutional layer. The results for the different layer types were similar, with differences of approximately 1%. The authors incorporated a Conv-LSTM module into the LADN component to effectively model spatial dependencies between feature vectors. This module processes data sequentially, line by line, across the image, allowing it to account for local context during anomaly detection. As a result, the network can more effectively identify subtle irregularities in the image structure, such as barely noticeable changes in texture or color, which may indicate manipulations. Another key element of LADN is the Z-score function, used to normalize features and highlight deviations from the norm. Z-score enables the identification of pixels or regions in the image that significantly differ from their surroundings, a hallmark of manipulations. After being processed by Conv-LSTM, the data are passed through additional convolutional layers, which further refine and integrate spatial information, enhancing the network's ability to distinguish complex manipulation patterns. During the training process of LADN, a loss function based on the difference between the predicted and actual localization masks was used, enabling the network to learn precise anomaly detection. The results presented by the authors demonstrate that ManTra-Net, leveraging LADN with the Conv-LSTM module, achieves high effectiveness in detecting and localizing forgeries across various datasets. The network not only handles individual types of manipulations but also copes with complex combinations of manipulations and previously unseen types of forgeries.