Images are often used as important evidence in legal proceedings, therefore their authenticity verification is of great significance.

Recently, the edge computing research group at Shenyang Institute of automation of the Chinese Academy of Sciences has developed a new technology of image forgery detection based on motion blur. The study was published on IEEE Sensors Journal.

After overcame a series of difficulties such as obtaining reliable motion blur kennels on small image patches and estimating the 3D motion path of the camera during exposure, the research group proposed a method that can significantly improve the forgery detection accuracy.

The parameterized kernel is used in this method to replace the non-parameterized kernel, so that the reliability of motion blur kernel estimation can be improved. The parameterized motion blur kernel is mapped into the 3D pose space of the camera to obtain the 3D pose set, and the intersection of the 3D pose sets (shared pose set) corresponding to multiple image patches is obtained.

By detecting the consistency between the projection kernel of the shared pose set on each image patch and its original estimated motion blur kernel, the image splicing forgery detection is carried out.

Besides, the research group proposed a series of algorithms such as motion blur kernel reliability detection and consistency propagation and conflict solution to improve the reliability and efficiency of detection as well as the accuracy of forged boundary segmentation. Compared with the state-of-the-art methods, the detection accuracy of the proposed method is improved by more than 20%.