<![CDATA[Cloud computing-like services that are great at paying for and managing multimedia are fundamental technological innovations that have made it easier for individuals and organizations to adopt multimedia content. Thanks to social media, different people with different perspectives can voice their opinions and present data through photos and videos. However, video tampering is a significant issue because illegal modification of video content can easily mislead audiences and make it difficult for them to relate to reality. This is, therefore, a serious problem, as the consequences of video forgery are dire. Several image processing-based solutions have emerged to address video forgery. Artificial intelligence has recently allowed deep learning models to be trained extensively; hence, deep learning has been frequently used for video tampering detection. However, further work is still required to refine such models or develop hybrid models to improve the existing models' capabilities in identifying video forgeries and assisting digital forensics. We introduce a framework based on deep learning to automate the detection and localization of video forgeries. We offer a hybrid deep learning model that fuses Inception V3 with a Gated Recurrent Unit (GRU) as part of our framework. We also propose a new algorithm, Intelligent Video Forgery Detection (IVFD), to detect the forgeries and their invariants based on this hybrid model. Through empirical studies applied on a standard dataset, called the Deepfake Challenge dataset, we get an accuracy of 97.21%, which makes our hybrid deep learning model outperform many existing models. Since video content is prevalent in almost all applications in today's era, our design system should be laid on top of these applications, which can facilitate detecting the tampering of the videos and thereby contribute towards digital forensics.]]>
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