An Efficient Technique for Detection and Localization of the Forgery in Digital Videos

Video forgery detection is challenging because small frame-level changes like insertion, duplication, and deletion, disturb the temporal motion continuity of video sequences. This paper proposes a reliable technique based on Flow-Strain Energy (FSE) for identifying and locating forged frames. The method simultaneously records local deformation and motion intensity in video sequences by combining strain tensor analysis and optical flow. By calculating dense optical flow between successive frames, optical flow energy and primary strain are estimated. A single FSE description that characterizes motion-deformation features is created by combining these elements. Statistical methods such as mean, standard deviation, and Z-score normalization are used to assess temporal variance in FSE. By clustering consecutive outlier frames that surpass an adaptive thresh- old, forged regions can be accurately localized. The suggested technique produces a localization accuracy of 94.21%, an F1-score of 97.03%, and an overall detection accuracy of 95.92%. The effectiveness of the proposed method in detecting frame-level forgeries and its resilience to changes in illumination and compression issues are demonstrated through experiments on real and altered videos. Furthermore, the framework is suitable for practical video forensic applications due to its excellent processing performance.