# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 1 Introduction  
Researchers have studied extremist strategies which had done between 70% and 80% of their networking online [1]. In addition, [PERSON] et al. [2] point out that \"the willingness to look more critically at the role of the media and the Internet is notably absent in current counter-terrorism efforts in most democratic states\". Radical groups have been using new propaganda techniques in the last few years. One that has been gaining continuous popularity is the joining of fragments of old terrorist attacks to make a seemingly new propaganda video. The aim of this work is to determine the sources of a newly released video efficiently and automatically.  
The number of terrorist attacks declined worldwide between 2006 and 2018. Although in 2018 there have been fewer terrorist attacks than in 2006, the number of victims was similar. As explained by UNODC (United Nations Office on Drugs and Crime) [3], even though terrorists have found many ways of using the Internet in furtherance of illicit purposes, their online activity also gives us the opportunity to gather intelligence and evidence for the prosecution of such acts. Image intelligence has successfully been used to identify radical profiles on Social Networks [4] and other methods for identifying radicalization on Twitter have also been developed in recent years [5].  
[PERSON] et al. [6] stated that even though web crawling may seem a breadth-first-search application, it indeed experiences a wide range of problems, from system concerns to theoretical questions. The management of a very large data structure requires the definition of a system for the classification and storage of information. Thus, in this research the defined structure allows us to recover online videos that contain terrorist propaganda, analyze them, and extract conclusions.  
As [PERSON] [7] describes: groups need to construct and maintain a collective identity, or more specifically, a belief in their own political or social efficacy. Radical groups assure the authenticity and novelty of their propaganda, which will be analyzed and whose original sources will be published. In addition, members of such groups trust their leaders and the fidelity of their propaganda. Their trust will be proven misguided.  
Big technological companies (like Facebook or YouTube) are rumored to have similar methods of removing extremist videos automatically [8], but there have been no academic publications on that topic yet.  
This paper combines web-scraping and video comparison to identify extremist sources automatically through the usage of proven methods. Having introduced the topic of this paper briefly, we further elaborate on it in the next six sections. In the context of locating the original sources of a new video, Section 2 briefly describes the algorithm used to compare videos and the criteria that are used to classify matching videos; videos match when the new video contains fragments of the source video. Section 3 describes the dataset used and the methodology details. Section 4 is related to the developed web crawler and its foundations. Section 5 describes the results and Section 6 provides some practical implementation details. Finally, Section 7 presents the conclusions and proposes future lines of research.  
Retrieved videos have been accepted as genuine and results are satisfactory. This method performs well in real life situations and could be implemented as part of a counter-terrorism strategy.