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The word Deep fake originated in 2017 from a Reddit user named “deep fakes”. In recent months, Free deep learning-based software tools has facilitated the creation of credible Face exchanges in videos that leave few traces of manipulation, in what they are known as "Deep Fake"(DF) videos. Manipulations of digital videos have been demonstrated for several decades through the good use of visual effects, recent advances in deep learning have led to a drastic increase in the realism of fake content and the accessibility in which it can be created. Creating the DF using the Artificially intelligent tools are simple task. But, when it comes to detection of these DF, it is major challenge. Because training the algorithm to spot the DF is not simple. We have taken a step forward in detecting the Deep Fake.

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Nowadays In banking and financial sectors major threat of Phishing is taking place. Phishing is a type of Internet scams that seeks to get users credentials from phishing websites such as passwords, credit card numbers, bank account details and other sensitive information. We use content based extraction technique for detecting phishing website based on the source code . Our aim is to detect the phishing pages by searching the similar web pages and it compares with HTML source code. We are using random forest classifier algorithm used to classify the website. Such a type of detection build confidence between bot use and internet community.

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Web Applications are liable to information security threats due to the compelling information it acquires from the users. Possessing data is the most powerful thing in this day and age. Wrongly acquired data can be used to exploit a company- which can be devastating, both in financial and reputational damage. The threats according to OWASP Top 10 include SQLi, Cross-Site Scripting (XSS), XXE, etc., In this project, we focus on building a tool- it uses ensemble learning algorithms to train the payloads (SQLi and XSS) and detect attacks when user gives input; if any such attacks are detected the public Ip address of the user will be blocked.

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Social media platforms provide a place where you can interact with any kind of people from all over the world which can broaden people’s mindset, understand other’s culture, etc. However, The misuse of these platforms has introduced a new form of aggression and violence called as cyber bullying. Cyber bullying is defined as willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices. There are many negative impacts on victims due to cyber bullying. There need to be a proper way to detect cyber bullying, to prevent further victims.

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Eye is the most vital organ for any living organism. There are many ocular diseases that may cause complete blindness if neglected. The current procedure for diagnosing ocular diseases is very complicated and time consuming. It requires expertise, resources to diagnose an ocular disease. Diagnosing an ocular disease is costly and they are not available to all people, especially to the rural people. The above disadvantages made a lot of people lose their sight. There is a need for an automated process to diagnose an ocular disease faster and requires less expertise. And the process should be cost efficient and available to all people. Building a Machine Learning model that takes an eye image as input and provides the ocular disease as output solves the issue of complexity in diagnosing an ocular disease. Building a website for the model and deploying it resolves the issue of availability.

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