# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 3 Materials and Methods
### Discrete Wavelet Transform
(**left**) Y-axis shows Accuracy during the proposed training (**right**) Y-axis shows loss during the proposed model training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model A** & **Model B** & **Model C** & **Merged Model** \\ \hline Training Accuracy & 80.73\% & 91.85\% & 71.06\% & 93.85\% \\ Validation Accuracy & 78.43\% & 88.81\% & 68.85\% & 88.91\% \\ Test Accuracy & 78.64\% & 68.02\% & 50.70\% & 89.59\% \\ Test ROC & 0.87 & 0.76 & 0.75 & 0.96 \\ Total Epochs & 125 & 148 & 117 & 152 \\ Best Epoch & 75 & 98 & 67 & 102 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance Metrics on Lowest Validation Loss Epoch and Total Training Epochs. formance. This concept is validated by the superior accuracy achieved by our final model compared to its constituent models, indicating the efficacy of using insight of passive methods to select streams for CNN analysis in a multi-stream model. The study's limitations primarily revolve around the size and scope of the dataset, coupled with the lack of comprehensive knowledge regarding the manipulations performed on the images. The absence of detailed documentation regarding the specific manipulations applied to each image impedes a thorough analysis of detection performance across different manipulation types. Furthermore, the study lacks a thorough statistical analysis and discussion of false positives/negatives. While the study demonstrates the potential for multi-stream CNN architectures, the absence of localization of manipulated areas represents a further avenue for exploration. Additionally, the study did not thoroughly explore the selection of optimal streams based on passive methods. However, the findings hold broader implications, highlighting the potential of multi-stream CNN architectures in image manipulation detection. By integrating diverse passive detection methods, this approach not only enhances detection accuracy but also underscores the importance of combining complementary techniques for robust detection. Moreover, the study emphasizes the necessity for standardized datasets and rigorous evaluation metrics in the field of image forensics. These insights can inform future research aimed at developing more effective and reliable methods for detecting manipulated images, thereby enhancing the integrity and trustworthiness of digital media. This research lays the groundwork for several avenues of expansion and real-world application. Firstly, further exploration could focus on enhancing the dataset used, incorporating more diverse and meticulously documented manipulations to improve the model's robustness and generalization capabilities. Additionally, future studies could delve into developing methods for localizing manipulated areas within images, which would significantly enhance the practical utility of image manipulation detection systems. Moreover, the multi-stream CNN architecture demonstrated in this research presents a promising framework for integrating various detection techniques. Expanding upon this, researchers could explore the selection of optimal streams based on passive methods, thereby refining the model's ability to detect a wide range of manipulation types with greater accuracy. In real-world scenarios, the findings of this research could be applied in various fields, including digital forensics, media authentication, and content moderation on social media platforms. By deploying robust image manipulation detection systems developed from this research, organizations and individuals can better safeguard against the dissemination of misleading or falsified visual content, thereby upholding the integrity and trustworthiness of digital media. Writing--original draft, Methodology, Software, Investigation and Formal analysis; A.L.A. ; Writing--original draft, Methodology, Formal analysis and Project administration; M.D.L. ; Writing--review & editing, Validation and Visualization; J.C.S.d.A. ; Writing--review & editing, Validation and Visualization; J.F.D.P.S.