Real-time object-removal tampering localization in surveillance videos by employing YOLO-V8

Abstract

Videos are considered as most trustworthy means of communication in the present digital era. The advancement in multimedia technology has made video content sharing and manipulation very easy. Hence, the video authenticity is a challenging task for the research community. Video forensics refer to uncovering the forgery traces. The detection of spatiotemporal object-removal forgery in surveillance videos is crucial for judicial forensics, as the presence of objects in the video has significant information as legal evidence. The author proposes a passive max–median averaging motion residual algorithm for revealing the forgery traces, successfully giving visible object-removal traces followed by a deep learning approach, YOLO-V8, for forged region localization. YOLO-V8 is the latest deep learning model, which has a wide scope for real-time application. The proposed method utilizes YOLO-V8 for object-removal forgery in surveillance videos. The network is trained on the SYSU-OBJFORG dataset for object-removal forged region localization in videos. The fine-tuned YOLO-V8 successfully classifies and localizes the object-removal tampered region with an F1-score of 0.99 and a precision of 0.99. The observed high confidence score of the bounding box around the forged region makes the model reliable. This fine-tuned YOLO-V8 would be a better choice in real-time applications as it solves the complex object-based forgery detection in videos. The performance of the proposed system is far better than the existing deep learning approach.