# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 3 Materials and Methods
### Discrete Wavelet Transform
; Supervision, Funding acquisition, and Resources; [PERSON]; Supervision, Funding acquisition, and Resources; V.R.Q.L. All authors have read and agreed to the published version of the manuscript. The authors would like to acknowledge the support provided by the Fundacao de Amparo a Pesquisa e Inovacao do Estado de Santa Catarina (Fapese) under the project FAPESC N\({}^{\circ}\) 29/2021-Programa Estruturante Academico-Apoio a Infraestrutura de Laboratorios Academico da Estado de Santa Santa Catarina. This support was partial and significantly contributed to the success of this research. The authors would like to acknowledge the Portuguese FCT program, Center of Technology and Systems (CTS) UIDB/00066/2020/UIDP/00066/2020, for partially funding the research. Finally, the authors would like to acknowledge the CAPES Finance Code 001. Also, partial funding for this research was provided by the CEREIA Project (# 2020/09706-7) Sao Paulo Research Foundation (FAPESP), FAPESP-MCTIC-CGI.BR in partnership with Hapvida NotreDame Intermedica Group. The data can be shared up on request. The data are not publicly available due to the Brazilian Law (LGPD). ## Conflicts of Interest: The authors declare no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} CID & Content identifier \\ CNN & Convolutional neural network \\ COV & Computer vision \\ DEL & Deep learning \\ DIF & Digital image forensics \\ DWT & Discrete Wavelet Transform \\ IMP & Image processing \\ FP & False positive \\ FN & False negative \\ HCI & Human-computer interaction \\ MLP & Multilayer perceptron \\ R-CNN & Region-based convolutional neural networks \\ RI & Region of interest \\ RPN & Region proposal network \\ TC & Totally connected \\ TF & True negative \\ TP & True positive \\ \end{tabular} 

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