## 2 Multi-Feature Detection Method of Image Tampering
From left to right are the color images, the chroma detection results of CCW, the results of the sharpness detection operator, and the ROI and RONI given by the CCW salient map. (**a**,**c**,**e**) Untampered images in Tampering ImageNet, Columbia, and CASIA ITDE V2.0, respectively; (**b**,**d**,**f**) tampered images in Tampering ImageNet, Columbia, and CASIA ITDE V2.0, respectively. ## 4 Discussion

In this paper, a multi-feature extraction-based algorithm for stitching tampered/untampered image classification is proposed based on pattern recognition technology. The algorithm takes chroma, sharpness, and texture differences and distortion statistics into consideration, and effectively reduces the incompleteness of the single-feature algorithm. However, the algorithm proposed in this paper only covers a limited range of image features, and it still cannot effectively identify the stitching tampering when the feature changes of the tampered area are insignificant. In a future work, this study will be extended to investigate the current limitations and further problems, such as how to cover a wider image feature range, and how to benefit from the cutting-edge deep learning technology to identify more subtle tampering and further improve the detection performance, which is also worthy of in-depth study. In addition, significant parts of the adopted tampering detection datasets are artificially constructed, so an expansion of the introduced dataset in this study and improvement of its contents to be more realistic and comprehensive for general applications in image tampering detection are also planned. Figure 13: Images that cannot be correctly detected by all the algorithms. From left to right are the color images, the chroma detection results of CCW, the results of the sharpness detection operator, and the ROI and RONI given by the CCW salient map. (**a–e**) Untampered images; (**f–h**) tampered images. ## 5 Conclusions

In this research work, a multi-feature extraction-based classification algorithm for color image stitching tampering was developed. In this system, three types of image feature extraction techniques were innovatively integrated to extract various image features, such as the chroma changes in multiple directions, sharpness features, texture distortion, and difference, namely, CCW chroma detection, sharpness detection, salient region detection, and NSS. A random forest classification algorithm was utilized to classify the extracted features. It was experimentally shown that the multi-feature-based extraction strategy can classify untampered and tampered images more accurately, and dramatically boost the detection accuracy on various benchmarked testing datasets. Compared to the related methods reported in the existing literature, higher classification accuracy values of 91.00%, 95.24%, and 88.02% were obtained using the Tampering ImageNet, Columbia, and CASIA ITDE V2.0 datasets, respectively. The proposed algorithm was also ranked the highest in terms of the other three evaluation metrics: precision, recall, and F1-score. The results prove that the introduced algorithm favorably outperformed the existing techniques, both qualitatively and quantitatively, and a state-of-the-art image tampering detection performance was shown. The proposed algorithm is an effective tool for tackling the growing challenges related to visual data forgery in many critical fields. Conceptualization, R.J. and J.Z. ; methodology, R.J.; software, R.J.; validation, R.J.; formal analysis, R.J.; investigation, R.J.; data curation, R.J.; writing--original draft preparation, R.J.; writing--review and editing, R.J., A.N., D.L. and J.Z. ; visualization, R.J.; project administration, J.Z. ; funding acquisition, D.L. and J.Z. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Natural Science Foundation of China under Grant 11827808 and 11974082. Not applicable.