Over the years, cybercriminals have found several methods to carry out their illegal activities, and the circulation of deepfakes is one of them. It is an offering of technological advancement that has become a great threat to people’s privacy.
Deepfake is a method of recreating someone’s identity with fake elements in the form of images, audio, and video. Several software and applications are available on the Internet with which people can easily produce such fake files. The practice can be hazardous as it spreads misinformation among the public, misleading them to misperceive events and individuals.
Sources state that the rate of deepfake creation and distribution has increased tenfold in 2024 compared to the previous year, leading to a remarkable rise in cybercrime. Considering such a significant surge, experts predict that the number will further enhance in the upcoming years. Let us understand what deepfake technology is, how does it work and what privacy concerns does it raise.
What is Deepfake Technology?
Deepfake technology, or synthetic media creation, is a segment of artificial intelligence (AI) that processes larger datasets to mimic user identity. A Reddit user introduced the term deepfake in 2017 combining ‘deep learning,’ a major component of synthetic media, with the term ‘fake’ to depict the practice of creating manipulated media.
In simple words, deepfakes are images, videos, and audio clips made with the support of AI that include artificial elements but look authentic. The technology processes big datasets to identify patterns in a real file and transform them into synthetic media. Though developers introduced deepfakes for casual entertainment purposes, their usage has heightened lately, creating privacy threats and fraud.
People with ill intentions create manipulated images and videos using deepfake technology of others and threaten them by sharing them on public platforms. Over the years, social media sites have been significant mediums for the circulation of synthetic media.
How Does Deepfake Technology Work?
Deepfakes rely on complex AI and ML algorithms that allow them to process complicated data faster and offer results quicker. Generative adversarial networks (GAN) play a major role in creating deepfakes, which include two crucial ML models or algorithms: generator and discriminator.
These algorithms allow the mechanism to attain deep learning capabilities that remarkably support pattern recognition. Let us understand the roles of the generator and discriminator in deepfake technology vividly:
Generator: The model creates an initial copy of the original file after processing it and identifying patterns.
Discriminator: The algorithm identifies the gaps between the authentic and generated files through which people can trace its synthetic elements.
The process of generator and discriminator continues till the gaps between original and fake files are unrecognizable. The method is also known as a variational auto-encoder (VAE) that encodes media files, adds synthetic components to them, and decodes high-quality fake files.
How Deepfakes Can Increase Privacy Concerns?
Deepfake technology can trigger remarkable privacy concerns among the public. Celebrities, politicians, and known personalities are the common targets of deepfake creators with harmful intent. The following are the general outcomes of deepfake circulation:
Identity theft: Deepfake technology poses a significant risk of identity theft, enabling criminals to exploit individuals' identities for illicit purposes. This manipulation of user identity with artificial elements can lead to the widespread dissemination of false information about them.
Defamation: Spreading misinformation on social networks can be a reason for defamation. In such a situation, people encounter the issue of character or identity assassination. Cybercriminals intentionally carry out such practices to harm their targets and destroy their reputation.
Financial fraud: Criminals often create synthetic media of people and threaten them with those for financial benefits. Such activities increase financial fraud.
Social engineering: Deepfakes are a typical source of social engineering scams. Criminals use wrong identities through voice notes and videos to fool people, asking for huge amounts of money and other valuable assets from the victims.
Concluding remarks:
A few years back, deepfakes were easily recognizable. However, recent technological advancements have made it harder to differentiate between authentic and fake media. Experts suggest staying informed with accurate information to eliminate the chances of being influenced by deepfakes. Read our articles to gain knowledge on mechanisms like deepfake technology.
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