Deep fake is a technology which allows for the creation and video making of real people. saying something and doing things they never said or did magnify the truth. Deepfake has garnered widespread attention for their potential use. in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financially. used in both entertainment and malicious activities like spreading misinformation and fraud.That’s how deep fake invent the term deep fake originated around 2017.
In the real world of artificial intelligence and digital media, few innovations have sparked as much fascination and controversy as deep fake technology. While deepfakes are often associated with illusionary and destructive uses, their establishment is rooted in a fascinating intersection of scientific curiosity and technological advancement. To understand why deep fake technology was invented? it is essential to explore its origins, the foundational technology behind it, and the motivations driving its development. The use of artificial intelligence to create hyper-realistic but fake images, videos, and audio, has gathered significant attention in recent years.The journey of deep fake technology begins with foundational research in AI and machine learning, specifically on the domain of deep learning and generative models.
Deep fake technology appears from the innovative work of researchers like Ian Goodfellow, who introduced Generative Adversarial Networks (GANs) in 2014. This innovative framework allowed computers to generate highly realistic synthetic media into two neural networks against each other: one creating content and the other evaluating its authenticity. However, the term “deepfake” and its popularisation can be traced back to a more controversial Reddit user known as “deepfakes” in late 2017, who used these advanced algorithms to create unrealistic face-swapping videos.
Deepfake technology was developed by looking beyond its most faceless applications. The technology’s invention was driven by a desire to push the boundaries of Artificial Intelligence, explore new creative possibilities, and enhance digital media capabilities. Unlike earlier days when the circulation of news required hours and days, in the contemporary world, information just needs seconds to reach people beyond boundaries with faster networks and engaging collaboration platforms. Under such circumstances, the impact of deep fakes or synthetic media can be unimaginable on people and society at large.
There are various types of synthetic media, such as photos, videos, and voice. Remarkably, more than 70% of the global public fails to identify such content, resulting in people losing trust in technology. Let us discuss how deep fakes in the media can impact the reliability and credibility elements of our society.
Deepfake technology itself does not have a single inventor, but it evolved from the work of several GANs that are the core technology behind deepfakes. Reddit user “deepfakes”.This user started sharing deep fake videos in late 2017, bringing widespread attention to the technology. when an internet forum user with the alias deepfake began posting manipulated videos .
What’s the motive for inventing this technology?
Deep fake technology has accumulate significant attention in recent years, both for its innovative potential and the ethical concerns it raises.Researchers and developers are motivated by the challenge of pushing the boundaries of what AI can achieve in generating realistic images, videos, and audio that closely mimic real human behaviour Scientists and developers are enthusiastic about the challenge of creating realistic images, videos, and sounds using artificial intelligence (AI)and machine learning. It’s about pushing the boundaries of what technology can do.
There are a number of critical problems that are yet to be resolved for existing DeepFake detection methods. The rapid developments of deep neural networks (DNNs) in recent years have made the process of creating convincing fake images/videos increasingly easier and faster. DeepFake videos first caught the public’s attention deep fake-generated those scenarios which can help them into training law enforcement, medical professionals, or even customer service representatives by providing lifelike, interactive environments due the Companies might use deep fakes to create personalised ads or entertainment content tailored to individual users.DeepFake detection has also been supported by government funding agencies and private companies.
In India, deep fakes gained momentum after a short clip of actor Rashmika Mandanna went viral last year. A week later, Prime Minister Narendra Modi also flagged the possible harms that could arise out of the misuse of the technology. It shows how easily digital content can be manipulated, helping people learn to spot fakes and avoid being deceived.In essence, the motives for deep fakes range from creative innovation and practical applications to more harmful purposes like misinformation and fraud. The technology has both exciting possibilities and significant risks, depending on how it’s used. educating the public about how to detect and respond to deep fake content.
This motivation is driven by a desire to build resilience against misinformation and protect society from potential harm. Deepfake technology is a double-edged brand, with motivations ranging from creative innovations and practical applications to more criminal uses like misinformation and cybercrime. The ongoing challenge is to harness its potential for good while mitigating the risks and ethical concerns it presents.DeepFakes that are automated and mass produced by AI algorithms. However, we need to be aware of these results. Although the organisers have made their best effort to replicate situations where
DeepFake videos are deployed in real life, there is still a significant inconsistency between the performance on the evaluation dataset and a more real dataset. In addition, all solutions are based on clever designs of DNNs and data augmentations, but provide little insight beyond the “black box”-type classification algorithms.
Furthermore, these detection results may not completely reflect the actual detection performance of the algorithm on a single DeepFake video, especially ones that have been manually processed and perfected after being generated from the AI algorithms. Such “crafted” DeepFake videos are more likely to cause real damages, and careful manual post processing can reduce or remove artefacts that the detection algorithms predicate on.
We discuss the pros and cons, as well as the potential pitfalls and drawbacks of each type of the solutions. We also provide an overview of research efforts for DeepFake detection and a systematic comparison of existing datasets, methods, and performances. And what was the motive of the deep fake technology ? Notwithstanding this progress, there are a number of critical problems that are yet to be resolved for existing DeepFake detection methods.The technology has both exciting possibilities and significant risks, depending on how it’s used.
