Video Killed the Text Star: OSINT Approach

Conference:  BlackHat EU 2018



The presentation discusses the importance of incorporating video analysis and facial recognition in Open Source Intelligence (OSINT) to keep up with the growing trend of video content on social networks.
  • Open Source Intelligence (OSINT) is valuable information collected from publicly available sources that can be used in decision-making.
  • Video content is growing exponentially and is expected to make up 80% of the world's internet traffic by 2021.
  • Facial recognition is a machine learning application that has had significant advances in recent years and can be used for security purposes.
  • Implementing a fast and scalable infrastructure using open source components can allow for the analysis of hundreds of hours of video.
  • An anecdote is given about how video processing helped identify the location and time of an ISIS propaganda video.
  • The six simple steps to understand facial recognition are: identifying where the faces are, obtaining landmarks, aligning the image, obtaining the vector that defines the face, comparing the vector with other faces in the database, and recognizing the face.
An example is given of how video processing helped identify the location and time of an ISIS propaganda video.


In 1979 The Buggles launched the hit song "Video Killed the Radio Star." Nowadays The Buggles could write a new song titled "Video Killed the Text Star." Social networks are growing around video content. This means that if OSINT (Open Source INTelligence) wants to stay alive needs to start getting value from video content. Video analysis, and person recognition in particular, is a very interesting task to security managers, CISOs, and analysts with responsibilities in physical security. However, video and image processing is gaining prominence. Internet content grows exponentially and there is a trend of publishing content in a video format. It's time to focus on this kind of information and start to move OSINT techniques and technologies to be able to process this type of information. Human face recognition is one of the Machine Learning applications which has had more advances in recent years. This advances and the actual power of hardware make it possible to implement this kind of service at a very low cost.We can use Open Source components to implement a fast and scalable infrastructure that allows you to analyze hundreds of hours of video in a simple way. The work that will be shown in this presentation will allow people to be identified and compared with a series of images of people to determine if these people appear or not in the recorded video images.



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