# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 3 Materials and Methods
### Discrete Wavelet Transform
The final proposed merged model is a culmination of individual models A, B, and C. Initially, the last non-output layers of models A and B were combined through a concatenation layer. Subsequently, this combined output was further merged with the last non-output layer of model C through another concatenation layer. To facilitate learning, two dense layers with 64 neurons each were added, accompanied by batch normalization layers. Notably, all layers of the individual models were frozen to preserve the acquired knowledge during training. While Dropout layers were initially considered, our experimentation revealed that Batch Normalization exhibited superior performance within the same training time, aligning with findings by [PERSON] et al. (2019). The optimizer employed was Adam, with accuracy serving as the primary metric during training. For the Loss function, Binary Cross Entropy was utilized. Rectified Linear Unit activation functions were applied across all layers, with the exception of the output layer, which employed the sigmoid function for classification purposes. ### Dataset Assembly

The initial step in executing our experiments involved compiling the final dataset. To achieve this, we meticulously curated authentic and manipulated images from four distinct datasets. These datasets were chosen for their robust representation of real-life scenarios, as they involved human manipulation of images, with an emphasis on creating manipulations that were challenging to detect. The size of the dataset was limited due to the low availability of humanly manipulated images, which require significant time and effort to create. Additionally, biases may have been introduced due to the skill set of individuals selected to manipulate the images. Nevertheless, efforts were made to ensure that biases were minimized by selecting datasets that inherently did not impose restrictions on the types of manipulations that could be performed. * CASIA V2.0: proposed in ([PERSON] et al., 2017), contains 7491 authentic images and 5123 manipulated images containing Splicing and or Duplication operations with retouching operations applied on top to mask alterations;

Figure 7: Illustration of the architecture used in the three individual models. * Realistic Tampering Dataset: Proposed by [55; 56] containing 220 authentic and 220 Splicing and or Duplication manipulations made to the original images with the objective of being realistic. Retouching operations are sometimes applied to help hide Compositing and Duplication manipulations. In addition, this dataset provides masks of tampered areas and information about capture devices used;
* IMD2020: Proposed by [57], it consists of four parts, first a dataset containing 80 authentic images manipulated to generate 1930 images tampered realistically and using all types of manipulation, with their respective manipulation masks. Then the second part consists of 35,000 authentic images captured by 2322 different camera models, the images were collected online and reviewed manually by the authors. The third has 35,000 algorithmically generated images with retouching manipulations. Finally, the last part has 2759 authentic images acquired by the authors with 19 different camera models designed for sensor noise analysis;
* CASIA V1.0: Proposed in [54], Contains 800 authentic images, 459 Duplicate-type manipulation images, and 462 Splicing images. This dataset has no retouching operations applied. By not imposing limitations on the number of manipulations used, certain types of manipulations may be overrepresented in the dataset. Furthermore, only one of the selected datasets provides information on the types of manipulation performed on each image, leaving uncertainty regarding the exact biases present. However, the hope is that the proportion of manipulations in the dataset mirrors that of real-world scenarios. To balance the dataset and incorporate realistically manipulated images, we divided the images into two folders. The first folder consists of 7491 authentic images from the CASIA V2.0 dataset.