Deepfake, the use of artificial intelligence in media editing, might be a dangerous phase of technology.
Deepfakes are convincingly real-looking photos, videos, and audio that is actually AI-manipulated fabrications. Deep learning, machine learning (ML), and artificial intelligence (AI) technologies are used to produce false material, such as superimposing a celebrity's face onto another person's body and having them say or do fictitious things to deceive viewers.
Deepfake technologies are becoming more advanced, allowing criminals to change the context of the story, potentially jeopardizing the legitimacy of information we're presented with online. The subject of how to recognize deepfakes is getting increasingly important as the number of deepfakes doubles every six months.
What are AI and Machine Learning?
The science and engineering of creating intelligent devices, particularly intelligent computer programs, is referred to as Artificial Intelligence. It's analogous to the problem of utilizing computers to study human intellect, but AI doesn't have to be limited to physiologically observable ways, whereas:
Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to emulate the way people learn, with the goal of steadily increasing accuracy.
With machine learning, IBM has a long history. With his study (PDF, 481 KB) (link lives outside IBM) on the game of checkers, one of its own, Arthur Samuel, is credited with coining the phrase "machine learning." In 1962, self-proclaimed checkers master Robert Nealey played the game on an IBM 7094 computer and lost. This achievement may appear little in comparison to what is possible now, yet it is regarded as a watershed moment in artificial intelligence. The technological advancements around us will continue to accelerate in the next decades.
Concerning the Deep Fake:
These are fake media in which a person's likeness is substituted with that of someone else in an existing photograph or video.
It employs sophisticated machine learning and artificial intelligence algorithms to modify or produce deceptive visual and audio information.
Deep learning is used to make deepfakes, and it entails training generative neural network designs like autoencoders and generative adversarial networks (GANs).
Deepfakes are a developing cultural, political, economic, social, and corporate concern with the potential to inflict harm, even if they appear as innocuous memes or brilliant marketing efforts in many circumstances.
Deepfakes have troubling consequences, ranging from disseminating disinformation and causing reputational damage to politicians and public figures. It also is used in corporate espionage and cyberattacks. Deepfake forums and websites are aplenty, with some even allowing customers to order unique deepfakes.
Advantages of Deep Fake
Deep Fake's advantages include giving individuals more independence by making accessible solutions smarter, more inexpensive, and more personalizable.
It can help a teacher offer compelling lessons, such as drawing historical people to life in the classroom in the education sector.
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It can bring pricey VFX technology to the masses.
Independent journalists and activists may use technology to achieve influence by reporting injustices on conventional social media platforms.
Deepfake may also be used to preserve people's privacy by masking the identities of their voices and faces. Computational Restoration & Public Safety tools like Al-generated synthetic media can help with crime scene reconstruction.
Corporate Level Fraud: Fraudsters use a phone call to induce businesses to make a money transfer. The caller impersonates the CFO or CEO.
Extortion of funds from businesses or individuals by transmitting modified media files containing deepfake, faces, and voices to depict persons making bogus remarks.
To disseminate fake news by fabricating a video with the face of a person with power in particular areas.
Pornography, women's abuse, and other heinous acts
Political Instability and Conflicts: According to research from the Canadian Communications Security Establishment, deepfakes might be used to meddle in Canadian politics, especially to denigrate leaders and sway voters.
Progress in Detecting Deepfakes in the Industry
Determining falsified media, according to Kaggle, is a technological issue that necessitates cross-industry collaboration. In recent years, research-driven projects have circulated to automatically detect various forms of deep fakes, which are frequently extremely difficult for people to detect.
The DeepFake Detection Challenge (DFDC), which was hosted on Kaggle and offered a $1 million reward to worldwide researchers who could develop breakthrough tools to help in identifying Deepfakes and manipulated media, was developed by AWS, Microsoft, Facebook, the Partnership on AI, and academia. Over 2,000 people took part, and over 35,000 deepfake detection models were created.
Microsoft has created a commercial deepfake detection tool that analyses video frames and gives a software confidence score that indicates whether the frame is real or artificial intelligence-generated. It was made available to several firms that were monitoring the 2020 US elections.
Intel and the Graphics and Image Computing group at Binghamton University collaborated on a program that leverages biological signals and data to accurately recognize and categorize deep fakes. The technology is founded on the premise that, while face recordings can be generated, physiological data such as heart rate variations and blood flow, which appear as pixel color changes, are difficult to replicate.
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How Does a Deepfake Get Made?
A variational auto-encoder (VAE) and a facial recognition algorithm are frequently used in deepfake movies. Visuals are encoded as low-dimensional representations, which trained VAEs to decode back into images.
In actuality, it would resemble the following hypothetical scenario:
- For a Super Bowl commercial, someone wants to construct a deepfake video of a popular entertainer.
- The individual employs two auto-encoders, one of which is trained on pictures of the entertainer's face and the other on a variety of facial images.
- Using a face recognition algorithm on movies that capture diverse postures and lighting settings, the training sets for each auto-encoder may be picked.
- Following the training, the two encoders are merged to create a lifelike video of the entertainer's face projected onto the body of another person.
Way Forward to AI
Improving Media Literacy: The most effective instrument for combating misinformation and deep fakes is media literacy for consumers and journalists.
Behavioural Change should be instilled in society to prevent such tragedies from occurring, and this should be incorporated into the school curriculum.
Technological interventions, such as Al-based detection systems capable of detecting deep fakes, must be developed as quickly as feasible.
A problem-specific legislative framework is urgently needed, and it should be based on a thorough investigation of the problem by an expert group and diverse stakeholders.
The Bottom Line
AI can be used to identify deepfakes and battle the harmful and immoral implications of malevolent deep fake technology, just as it can be used to generate them. As deepfakes grow more popular, this will be critical in reducing the hazards associated with falsified data. To learn more about deep learning, you can take up a free online course on deep learning for up-to-date knowledge on current breakthroughs and emerging technologies.