# Deep Learning-Based Digital Image Forgery Detection System
## 6 Conclusions  
Image forgery detection is a very challenging problem. In this era of technological advancement, we need to be able to distinguish between real and tampered images. In this study, we proposed a deep learning-based approach for image forgery detection. The proposed model is based on ResNet50v2 architecture, which uses residual layers; thus, using this architecture increases the detection rate of tampered images. Using this approach also provides the benefit of transfer learning by using the pre-trained weights of the YOLO CNN model. The use of transfer learning enabled us to train our model more efficiently, as we initialized our proposed model by meaningful assigning weights. This reduced the training time and complexity of the model and makes the architecture more efficient. We evaluated our proposed architecture on benchmark datasets, CASIA_v1 and CASIA_v2. We also compared the performance of our system with and without the use of transfer learning. We obtained an accuracy of 99.30% with the CASIA_v2 dataset for the forgery detection problem. The results of the comparison with the existing methods show the superiority of the proposed system. The proposed system will help in the image manipulation detection domain and also paves the way for future research in detecting multiple types of image forgery manipulations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Tampering** & \multirow{2}{*}{**Methodology**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Advantages/Disadvantages**} & \multirow{2}{*}{**Accuracy**} \\ \cline{5-6}  & **Targeted** & & & & Advantage: \\  & & & & The proposed model uses & \\  & & & & SVM as a classifier to attain & \\
[22] & Attacks that are a combination of transformations & AlexNet Model, CNN & MICC-F220 & good accuracy. & 93.94\% \\  & & & & Disadvantage: & \\  & & & & Less suitable for other & \\  & & & & datasets. & \\ \hline
[18] & Median filtering, AWGN, Gaussian blurring & CNN and error filter predictions & Images taken from 12 unique cameras & Advantage: & \\  & & & & The proposed model uses a & \\  & & & filter predictions & 12 unique cameras & CNN for manipulation & \\  & & & & detection and attains good & \\ \hline
[36] & Cut/paste and median filtering & CNN and Median filtering residuals & Boss base, UCID, Dresden, BOSS RAW, NRCS Gallery & Advantage: & \\  & & & & The proposed model gives & \\  & & & & Desaden, BOSS & \\  & & & & RAW, NRCS & \\  & & & & Gallery & \\  & & & & datasets. & \\ \hline
**Our** & & & & Advantage: & \\
**Proposed** & Image splicing & ResNet50v2 and YOLO weights & CASIA\_v1, CASIA\_v2 & Reduces training time and & \\  & & & & & uses ResNet-based & \\  & & & & architecture. & \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._  
**Author Contributions:** Conceptualization, E.U.H.Q. and T.Z.; data curation, E.U.H.Q., T.Z., and A.A.; formal analysis, E.U.H.Q., T.Z., and A.A.; funding acquisition, A.A.; methodology. E.U.H.Q.; project administration, T.Z. and A.A.; resources, A.A.; software, E.U.H.Q.; supervision, T.Z. and A.A.; validation, E.U.H.Q., T.Z., and A.A.; visualization, E.U.H.Q.; writing--original draft, E.U.H.Q.; writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Security Research Center at Naif Arab University for Security Sciences (Project No. SRC-PR2-01).  
**Acknowledgments:** The author would like to express their deep thanks to the Vice Presidency for Scientific Research at Naif Arab University for Security Sciences for their kind encouragement of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.