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If the image is likely a photo with human faces, OLAF sends the image to the Azure Computer Vision API for more advanced classification. Then it determines if the image is likely to contain a human face using the Viola-Jones object detection framework.

First, it determines if the image likely contains text using the Tesseract OCR library.

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When a file download of an image is detected, OLAF runs different image classification algorithms on the artifact. OLAF runs in the background monitoring the user's browser and the files he or she is downloading to specific folders. Although envisoned as a desktop Windows application, nothing in the core libraries is desktop or Windows specific and OLAF can conceivably be run on other desktop platforms like Linux or on mobile phones using Xamarin.

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Open-source user monitoring software like OLAF can provide a somewhat more ethical and secure way of user monitoring where such monitoring does have a legitimate purpose and value. You can count at least ten commercial user monitoring applications available for purchase today and the number of commercial companies making proprietary user monitoring software is only growing.

It's way better to learn about how something like user-activity monitoring can be done and publishing it as open-source means other people can study it and verify that at least the information being collected about the user's activity is being used in a legitimate way. But as far as technology is concerned, the most reliable position is that if something can possibly be done then someone is going to do it so worrying about the misuse of the things you create is largely pointless.

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Whether on a public PC or not, no one likes the idea of a program watching over their shoulder at what they are looking at online. Of course technological issues aside, OLAF seems to be just a sophisticated way of spying on a user.

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Like many Azure services, the Computer Vision API has a free tier of usage so anyone with a valid Azure subscription including the free Azure for Students subscription can try it out or run an application like OLAF. Setting up an Azure Cognitive Services resource is pretty simple and can be done in five minutes from the Azure Portal: I won't talk much here about image classification algorithms or setting up the Azure Cognitive Services accounts, rather I will focus more on the process of putting together different image classification and machine learning software components and the design decisions and tradeoffs that arise in this process. I will talk about using two popular open-source machine learning libraries - Tesseract OCR and Accord.NET as well as using a cloud-based machine learning service - the Computer Vision API from Azure Cognitive Services - to analyze and classify images. In this article, I will describe some of the steps and challenges in building a real-world image classification application in. For businesses, OLAF can detect the infiltration of documents which may contain the intellectual property of competitors and put the organization in legal jeopardy. OLAF captures the precise date and time an image artifact was created on a PC together with the artifact itself and attributes describing the content of the image which can then be used to provide forensic evidence for an investigation into violations of computer use policies.ДЇor PCs in places like libraries or schools or internet cafes, OLAF can help enforce an organization's computer use policies on intellectual property and piracy, adult or sexual images, hate speech or incitements to violence and terrorism, and other disallowed content downloaded by users. For images that are not photos, OLAF also runs OCR on the image to extract any text and sends this to Azure Cognitive Services' Text Analytics API to extract information regard things like the entities mentioned. Photographic images are sent to Azure Cognitive Services' Computer Vision API for analyzing and classifying the content including whether or not the photo may contain sexual or adult content.

OLAF monitors browsers and well known user folders for file-system activity and uses a two-stage detection process that first runs image object-detection algorithms to determine if a downloaded image is text-rich or is likely a photo containing human faces. OLAF ( Online Automated Forensics) is an automated digital forensics tool which uses computer vision and machine learning to automatically classify images and documents a user downloads while browsing the internet.