A Two-Phase Scheme by Integration of Deep and Corner Feature for Balanced Copy-Move Forgery Localization

In the era of Industry 4.0, the widespread application of digitization, automation, and Internet technology in industrial production has led to a significant increase in image data. Image security has become crucial because images are at risk of being tampered with at any time. To protect its authenticity, this article proposes a two-phase scheme to achieve balanced performance between accuracy and speed for copy-move forgery detection. Our scheme is divided into detection and localization phases. In the detection phase, the deep features are utilized to calculate the inner similarity. To improve the accuracy, a corner point matching technique is performed on the localization phase as a refinement step. The experimental results demonstrate the average $F1$-score is 0.6334 on CASIA2.0, making a 14.16% improvement. The computation time for each image is only 0.791 s in average. It has great significance in protecting the reliability and authenticity of industrial data.