Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

Inter-frame tampering in surveillance videos undermines the integrity of video evidence, potentially influencing law enforcement investigations and court decisions. This type of tampering is the most common tampering method, often imperceptible to the human eye. Until now, various algorithms have been proposed to identify such tampering, based on handcrafted features. Automatic detection, localization, and determine the tampering type, while maintaining accuracy and processing speed, is still a challenge. We propose a novel method for detecting inter-frame tampering by exploiting a 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatic feature extraction, employing an autoencoder to significantly reduce the computational overhead by reducing the dimensionality of the feature’s space; analyzing long-range dependencies within video frames using long short-term memory (LSTM) and gated recurrent units (GRU), which helps to detect tampering traces; and finally, adding a fully connected layer (FC), with softmax activation for classification. The structural similarity index measure (SSIM) is utilized to localize tampering. We perform extensive experiments on datasets, comprised of challenging videos with different complexity levels. The results demonstrate that the proposed method can identify and pinpoint tampering regions with more than 90% accuracy, irrespective of video frame rates, video formats, number of tampering frames, and the compression quality factor.