INDFORG: Industrial Forgery Detection Using Automatic Rotation Angle Detection and Correction

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber-physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose INDFORG, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to +/- 89 degrees. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation.

Automated summary

The article introduces an efficient algorithm for detecting and correcting forgery in industrial images, which does not require digital signatures or watermarks. By using basic geometric concepts and intensity correlation computation, the algorithm accurately detects rotation angles in industrial images. Experimental results show that the algorithm is highly efficient and preferred for trustworthy media delivery in industrial automation.