# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 3 Materials and Methods
### Discrete Wavelet Transform
Figure 11: Proposed Model area under curve (ROC) for the training and test datasets. (**left**) Results obtained on the validation dataset. (**right**) Results obtained on the test dataset. Figure 10: Model C area under curve (ROC) for the training and test datasets. (**left**) Results obtained on the validation dataset. (**right**) Results obtained on the test dataset. Figure 9: Model B area under curve (ROC) for the training and test datasets. (**left**) Results obtained on the validation dataset. (**right**) Results obtained on the test dataset. Figure 14: Model C Training Data, the lines in blue refer to results on the 80% of images used for training while those in orange refer to the 20% of images dedicated to validation. The X-axis shows the current training generation. (**left**) Y-axis shows Accuracy during model C training (**right**) Y-axis shows loss during model C training. Figure 12: Model A Training Data, the lines in blue refer to results on the 80% of images used for training while those in orange refer to the 20% of images dedicated to validation. The X-axis shows the current training generation. (**left**) Y-axis shows Accuracy during model A training (**right**) Y-axis shows loss during model A training. Figure 13: Model B Training Data, the lines in blue refer to results on the 80% of images used for training while those in orange refer to the 20% of images dedicated to validation. The X-axis shows the current training generation. (**left**) Y-axis shows Accuracy during model B training (**right**) Y-axis shows loss during model B training. The culmination of these analyses is encapsulated in Table 3, which details the final results achieved after activating the predetermined stop conditions, offering a comprehensive overview of model efficacious and operational efficiencies. ## 5 Conclusions and Future Work

This study provides a comprehensive review of passive forensic methods designed to detect manipulated images, emphasizing their underlying principles and efficacy. Building upon this foundation, we employ a novel approach that integrates two distinct detection methods within a multi-stream convolutional neural network (CNN) framework. This innovative methodology allows us to harness the strengths of each individual method while mitigating their respective limitations, thereby enhancing overall detection accuracy. Moreover, our analysis is conducted on a meticulously curated dataset comprising a diverse range of image manipulations, ensuring the robustness and generalizability of our findings. Through this combined effort, we aim to advance the field of image manipulation detection by offering a unified and effective solution capable of addressing real-world challenges. After training, our novel merged method demonstrated a remarkable accuracy of 89.59% on validation images, showcasing the advantages of our multi-stream CNN approach compared to individual streams. In contrast to models utilizing singular detection methodologies, such as Error Level Analysis (ELA) and the method proposed by [18], which were employed as separate streams in our multi-stream model, our merged model showcased superior performance. This comparison highlights the advantage of analyzing multiple streams concurrently, showcasing the synergistic effects of integrating diverse detection methods within a unified framework. Additionally, the individual streams achieved the following accuracies: the model trained solely on original images obtained an accuracy of 78.64%, while the ELA-based model achieved 68.02%, and the model utilizing the method proposed by [18] achieved 50.70%. The amalgamation of differently trained models on the same image dataset highlights the potential of leveraging various passive detection techniques to improve overall per

Figure 15: Proposed Model Training Data, the lines in blue refer to results on the 80% of images used for training while those in orange refer to the 20% of images dedicated to validation. The X-axis shows the current training generation.