# Detection of Tampering by Image Resizing Using Local Tchebichef Moments
## 5 Conclusions  
Content-aware image re-targeting methods are widely adopted to resize images to display on all kinds of terminals. However, they can be also used to make fake images, which don't have any perceptual annoying distortions. By the principle analysis of the three image resizing methods, we found that the correlation between adjacent pixels can be destroyed in the process of image resizing. Tchebichef moments have been extensively applied in field of image/vedo such as information security [18], pattern recognition [19] and image quality assessment [20], and so on. Inspired by this, it can be found from experiments that LTM can effectively reflect the correlation changes between adjacent pixels. We proposed a passive forensics algorithm based on LTM to identify tampered images obtained by content-aware image resizing methods in this paper. Our experimental results showed that our proposed method can obtain a better accuracy. It is verified that it has a good performance for the image resizing with high scaling ratios. In the future, we will try to evaluate the detection accuracy on the tampered images obtained by image resizing with low scaling ratios. In addition, our proposed method could not obtain a satisfied detection accuracy on the re-targeted images with JPEG compression. We will further analyze the tampered trace of the resized images with JPEG compression. In addition, since there are still a few image resizing methods [21,22] besides the three methods proposed in this paper, we will attempt to distinguish these image resizing techniques for re-targeted images by applying other multi-class classifiers [23, 24, 25, 26, 27, 28] and designing more general features from image/video processing methods [29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. In view of the importance of social media digital images in practical applications, research on their authenticity, integrity and traceability has been one of the hot and challenging research topics in the field of information security. We will adopt network optimization methods [39, 40, 41, 42, 43, 44, 45, 46, 47, 48] to improve the real-time and high efficiency performance of the feature extraction phases.  
Conceptualization: D.Z. and S.W.; investigation: J.W.; methodology: D.Z. and S.W.; software: D.Z. and S.W.; supervision: F.L.; validation: A.K.S. and V.S.S.; writing--original draft: D.Z. and S.W.; writing--review and editing: [PERSON] and A.K.S.  
This research was funded by the National Natural Science Foundation of China (61772454, 61811530332, 61811540410, 61772087, 61232016), the Scientific Research Fund of Hunan Provincial Education Department of China (14C0029) and the \"Double First-class\" International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology (No. 2018 IC25).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Uncompressed (\%)**} & \multicolumn{4}{c}{**Compressed (\%)**} \\ \hline
10\% & 20\% & 30\% & 40\% & 50\% & mixed & 10\% & 20\% & 30\% & 40\% & 50\% & mixed \\
84.04 & 91.02 & 94.32 & 95.41 & 96.04 & 93.04 & 70.93 & 74.59 & 79.63 & 83.18 & 85.34 & 76.09 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average detection accuracies of our proposed approach without preprocessing.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Uncompressed**} & \multicolumn{4}{c}{**Detection Accuracy (\%)**} & \multirow{2}{*}{**Compressed**} & \multicolumn{4}{c}{**Detection Accuracy (\%)**} \\ \cline{2-2} \cline{4-11}  & **OR** & **SNS** & **SC** & **SL** & & **OR** & **SNS** & **SC** & **SL** \\ \hline OR & 96.39 & * & * & * & OR & 87.98 & * & * & * \\ SNS & * & 91.54 & 9.21 & 3.21 & SNS & * & 77.25 & 16.29 & 12.49 \\ SC & * & * & 85.71 & 5.41 & SC & 1.35 & 1.73 & 65.14 & 17.07 \\ SL & 3.60 & 8.40 & 5.08 & 91.38 & SL & 10.67 & 21.02 & 18.57 & 70.73 \\ \hline \hline \end{tabular}
\end{table}
Table 5: CMOMTUI and CMOMTCI without preprocessing.