FakeBuster

The Indian Institute of Technology in Punjab’s Ropar on Wednesday said that the institute along with Australia based Monash University has developed a unique detector named ‘FakeBuster’ to detect imposters attending a virtual conference without anybody’s knowledge.

The detector can also find out faces manipulated on social media to defame or make joke of someone, said an official statement.
This standalone solution enables a user to detect if another person’s video is manipulated or spoofed during a video conferencing, it added.
The IIT team asserted that ‘FakeBuster’ is one of the first tools to detect imposters during video conferencing using DeepFake detection technology.
The device has already been tested and would hit the market soon.
The deepfake detection tool ‘FakeBuster’ works in both online and offline modes.
Since the device can presently be attached with laptops and desktops only “we are aiming to make the network smaller and lighter to enable it to run on mobile phones-devices as well.

Sophisticated artificial intelligence techniques have spurred a dramatic increase in manipulation of media contents.
The usage of manipulated media content in spreading fake news, pornography and other such online content has been widely observed with major repercussions.
Such manipulations have recently found their way into video-calling platforms through spoofing tools based on transfer of facial expressions.
These fake facial expressions are often convincing to human eye and can have serious implications.
These real time mimicked visuals (videos) known as deepfakes can even be used during online examinations and job interviews.
In the present scenario of pandemic when most of the official meetings and work is being done online, this standalone solution enables a user to detect if another person’s video is manipulated or spoofed during a video conferencing

Back to Basics

Deepfakes (a portmanteau of “deep learning” and “fake”) are synthetic media in which a person in an existing image or video is replaced with someone else’s likeness. While the act of faking content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content with a high potential to deceive.
The main machine learning methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders or generative adversarial networks (GANs).
Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.
This has elicited responses from both industry and government to detect and limit their use.

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