Real time object-based video forgery detection using YOLO (V2)

Video forgery detection is a challenging task nowadays due to fake video forwarding. Copy-Move type of attack is especially mostly practiced to tamper with the original contents of a video or an image. Copy-Move attack mainly deals with object-based video forgery. Traditional methods are quiet slow and not strong enough to detect complex Copy-Move attacks. So, automatic tamper detection in videos related to speed and accuracy is a challenging task. This paper proposes a new approach for the detection of Copy-Move attack in passive blind videos. Object-based forgery detection approach is implemented using fast and realtime object detector You Only Look Once-Version 2 '':YOLO (V2). The system is trained on the benchmark dataset videos for the automatic detection of forged objects within the video with a 0.99 confidence score. Trained YOLO (V2) model is accurately able to classify and localize the forged and non-forged objects within the given input video. The results and experimental analysis demonstrates that the proposed YOLO (V2) model achieved excellent results for detecting plain and complex Copy-Move attacks such as scaling, rotation, flipping. The performance excellent for object-based forgery detection for speed and accuracy than existing similar state-of-art deep learning approaches. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper presents a new method based on the YOLO (V2) model, successfully detecting Copy-Move attacks with good outcomes in terms of speed and accuracy.