<![CDATA[The widespread accessibility of inexpensive mobile phones, digital cameras, camcorders, and security closed-circuit television (CCTV) cameras has resulted in the integration of filmmaking into our everyday existence. YouTube, Facebook, Instagram, and Snapchat are a few of the video-sharing and editing applications that facilitate the process of uploading and editing videos. Additional instances include Adobe Photoshop, Windows Movie Maker, and Video Editor. Although editing has its advantages, there is a potential risk of counterfeiting. This occurs when films are edited with the intention of misleading viewers or manipulating their perspectives, which can be particularly troublesome in judicial procedures where recordings are submitted as evidence. The issue has been exacerbated by the emergence of deep learning methods, such as deepfake videos that effectively manipulate facial characteristics. Consequently, individuals have become less reliant on visual evidence. These issues emphasise the pressing necessity for the creation of dependable methods to determine the authenticity of films and identify cases of fraud. Contemporary methods can depend on assessing modified frames or utilising distortions generated during video codec compression or double compression. Since 2016, multiple studies have been undertaken to investigate techniques, strategies, and applications to tackle this problem. The objective of this survey study is to provide a comprehensive analysis of these algorithms, highlighting their advantages and disadvantages in detecting different forms of video forgeries.]]>
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