In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD , a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.

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Frequency domain techniques such as Discrete Cosine Transform (DCT) and Warped Discrete Cosine Transform (WDCT) ensures high accuracy when compared with the spatial domain techniques. Therefore, these image steganographic methods were evaluated using public datasets to compare the performance of DCT and WDCT. After performing different tests using the datasets in each of the algorithms, a comparative analysis is made in terms of the Peak Signal-to-Noise Ratio (PSNR) metrics. The results indicate that the stego image generated after embedding the secret acquires high imperceptibility and robustness. The performance of the WDCT algorithm is higher as compared to the DCT algorithm and the resultant images produced are very less prone to noise attacks. In DCT and WDCT algorithms, the cover image will be split based on 8×8 pixel blocks and 2D DCT is applied on each pixel. The secret will be embedded inside DCT coefficient and inverse 2D DCT is applied to recover the secret. Therefore, these image steganographic techniques can be adopted to transfer the confidential messages in different sectors. In the future, other data hiding methods using deep learning could be implemented to increase the robustness and imperceptibility of covert messages.

The advancement of Internet technology has significantly impacted daily life, which is influenced by digital videos taken with smartphones as the most popular type of multimedia. These digital videos are extensively sent through various social media websites such as WhatsApp, Instagram, Facebook, Twitter, and YouTube. The development of intelligent and simple editing tools has favoured the transformation of multimedia content on the Internet. As a result, these digital videos' credibility, reliability, and integrity have become critical concerns. This paper presents a video forgery (Copy-move forgery) dataset in which 250 original videos are manipulated mainly by two forgery techniques: Insertion and Deletion. Inserting transparent objects into the original video without raising suspicion is one type of manipulation performed. Another type of forgery presented on the dataset is the removal of objects from the original video without notifying the viewers. The videos were collected from five different mobile devices, namely, IPhone 8 Plus, Nokia 5.4, Samsung A50, Xiomi Redmi Note 9 Pro and Huawei Y9-1. The forged videos were created using a popular video editing software called Adobe After Effects as well as usage of other software such as Adobe Photoshop and AfterCodecs. Since the source of the videos is known, PRNUbased methods can be suitable for applying to the dataset. Experiments were performed using classical and deep learning methods. The results are recorded and discussed in detail, showing that improvements are essential for the dataset. Furthermore, the forged videos of this dataset are comparatively large when compared to other datasets that performed copy-move forgery.