An Extensive Approach for Inter-Frames Video Forgery Detection

The increasing prevalence of manipulated videos across various domains highlights the critical need for effective video forgery detection methods. In parallel, the demand for authentic and trustworthy images grows, emphasizing the importance of detecting digital image forgery in our society. Blind tampering has emerged as a prominent trend in visual content manipulation. This paper presents a comprehensive investigation that addresses the diverse challenges faced in previous research studies. Recent advancements in neural network-based approaches have shown remarkable efficiency in detecting image forgery by uncovering concealed characteristics within images, thereby enhancing accuracy. In this work, an extensive inter-frames video forgery detection approach is used. The primary goal is identifying and detecting manipulation between frames in a video sequence. The report examines techniques for detecting forgeries in images and the challenges posed by inter-frame and intra-frame fakes in videos. Also, emphasis is placed on frequently utilized datasets in this field, which can assist new researchers exploring this study area. Experimental results demonstrate the proposed approach's efficiency and robustness, highlighting its remarkable accuracy in detecting inter-frame video forgeries. This contribution to the field of video forensics provides a valuable tool for verifying the integrity and authenticity of video content.