Security in different applications is closely related to the goodness of the sequences generated for such purposes. Not only in Cryptography but also in other areas, it is necessary to obtain long sequences of random numbers or that, at least, behave as such. To decide whether the generator used produces sequences that are random, unpredictable and independent, statistical checks are needed. Different batteries of hypothesis tests have been proposed for this purpose. In this work, a survey of the main test batteries is presented, indicating their pros and cons, giving some guidelines for their use and presenting some practical examples.

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The mobile device ecosystem has dramatically evolved over the last few years, since users have openly embraced a massive use of mobile phones for different purposes: professional use, personal use, etc. Digital videos can be used to define legal responsibilities or as part of the evidence in trials. The forensic analysis of digital videos becomes very relevant to determine the origin and authenticity of a video in order to link an individual with a device, place or event. The field of forensic analysis of digital videos is constantly facing new and direct challenges. Even though the basic principles of this discipline remain unchanged, numerous issues appear every year that require new procedures and tools. Therefore, it is necessary to provide forensic analysts with techniques to identify the origin of multimedia content. In this paper, the topic of source identification in open scenarios will be discussed, since analysts do not know in advance the set of cameras to which a video belongs so they find it difficult to identify its source. This approach is similar to real-life situations since in most cases, analysts are unaware of the set of video cameras. This paper aims to create a technique that identifies the source of digital videos generated by digital devices through the use of unsupervised algorithms based on the analysis of the structure of multimedia video devices. INDEX TERMS Acquisition source identification, clustering analysis, container atoms, forensics analysis, video container.

The generation of random values corresponding to an underlying Gamma distribution is a key capability in many areas of knowledge such as Probability and Statistics, Signal Processing or Digital Communication, among others. Throughout history, different algorithms have been developed for the generation of such values and advances in computing have made them increasingly faster and more efficient from a computational point of view. These advances also allow the generation of higher quality inputs (from the point of view of randomness and uniformity) for these algorithms that are easily tested by different statistical batteries such as NIST, Dieharder or TestU01 among others. This article describes the existing algorithms for the generation of (independent and identically distributed-i.i.d.-) Gamma distribution values as well as the theoretical and mathematical foundations that support their validity.