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Browser sniffing (also known as browser detection) is a set of techniques used in websites and web applications in order to determine the web browser a visitor is using, and to serve browser-appropriate content to the visitor. It is also used to detect mobile browsers and send them mobile-optimized websites.
Feature detection (also feature testing) is a technique used in web development for handling differences between runtime environments (typically web browsers or user agents), by programmatically testing for clues that the environment may or may not offer certain functionality.
On the Web, a user agent is a software agent responsible for retrieving and facilitating end-user interaction with Web content. [1] This includes all web browsers , such as Google Chrome and Safari , some email clients , standalone download managers like youtube-dl , and other command-line utilities like cURL .
The user agent string format is currently specified by section 10.1.5 of HTTP Semantics. The format of the user agent string in HTTP is a list of product tokens (keywords) with optional comments. For example, if a user's product were called WikiBrowser, their user agent string might be WikiBrowser/1.0 Gecko/1.0. The "most important" product ...
Around the same time, the specifications for how web browser would be handling HTTP Client Hints on the web was published as a draft in a W3C Community Group Report. [2] In 2020, Google announced their intention to deprecate user-agent (UA) declaration by the browser. [4]
Netscape Browser (Netscape 8), which used MSHTML to render web pages in IE mode; Pyjs, a python Widget set Toolkit. Embedding IWebBrowser2 as an Active-X component and accessing the COM interface, Pyjs uses MSHTML for the Desktop version, through the python Win32 "comtypes" library. RealNetworks RealPlayer, a multimedia player app; Sleipnir, a ...
Browser isolation technologies approach this model in different ways, but they all seek to achieve the same goal, effective isolation of the web browser and a user's browsing activity as a method of securing web browsers from browser-based security exploits, as well as web-borne threats such as ransomware and other malware. [1]
In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames. [241] DARPA gave 68 million dollars to work on deep-fake detection. [241] Audio deepfakes [242] [243] and AI software capable of detecting deep-fakes and cloning human voices have been developed. [244] [245]