You have seen a Deepfake video, if you were not aware of it. Computer-generated Tom Cruises have appeared over the internet in recent years. Mark Zuckerberg is another regular target, with tapes spreading of him stating things he did not utter. Deepfakes are not a new issue, but the tools required to produce them are becoming accessible and sophisticated.
Deepfake statistics are discussed in this article. Deepfakes are harmful because they make it harder to believe what we see and hear online. The risk of abuse and harm to consumers, governments, and businesses cannot be emphasized. Despite the mounting danger that deepfakes pose to society, individuals are still unaware of what they are.
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Deepfakes are movies or photos made using AI-powered deep learning algorithms that depict individuals saying and doing things they did not say or do. Deepfakes are being used to conduct cybercrime, whether for financial gain, social disturbance, and election fraud. Deepfakes are used to conduct fraud and acquire access to services by pretending to be someone else.
They may be used for a variety of purposes, including synthetic identity fraud, new account fraud, and account takeover fraud. Deepfakes, known as face swaps, re-enactments, or Generative Adversarial Networks (GANs), may be employed in a variety of threat categories, including presentation and digital injection attacks.
Adoption Statistics for Generative AI
This page intends to include a wide range of generative AI-related statistics. We have divided the information into manageable subsections, beginning with a look at how companies and people are adopting various technologies.
ChatGPT’s Domain Peaked with 1.81 Billion Visits in May 2023
According to Similarweb research, OpenAI’s ChatGPT chatbot launched on November 30, 2022, and had 15.5 million visits in its first week. Visit rates increased, with 1.81 billion visits in May 2023 alone. According to the statistics, the second popular month was October 2023, with an estimated 1.7 billion visits.
One-Third of Survey Respondents Use Generative AI Often
According to McKinsey’s April 2023 poll, one-third of respondents said they utilize generative AI technology often in at least one business function. 37% of C-suite executives said they utilize the tools on a daily basis.
40% of Businesses Expect to Invest in AI Due to Gen AI Advancements
Generative AI is a high-priority subject for business executives. According to McKinsey’s study, 28% of respondents believe it is a priority on their boards’ agenda. Two out of five poll respondents expect their firms to expand their general AI investments as generative AI improves.
88.3% of Companies Plan to Roll Out Gen AI-Related Policies
According to AI Infrastructure Alliances research, the majority of enterprises are looking forward to using generative AI. Almost nine out of ten businesses plan to create regulations governing its adoption and use. Several issues should be resolved before any of this may occur.
While 41.8% of respondents indicated they were staffed up and had the right budget to deliver on promises of LLMs and generative AI, 58.8% answered the contrary. It is critical for organizations to remain at the forefront of their sector. It is critical to take your time and ensure you have your pieces in place.
Millennials and Gen Z Account for 65% of Generative AI Users
It is surprising that younger generations are open to generative AI technology. According to a Salesforce poll of customers in the United States, United Kingdom, Australia, and India, 7 out of 10 Gen Z respondents utilize generative AI.
If generative AI technologies can alleviate some of the decision-making fatigue, it is good. There are several worries about these technologies, not the least of which is the possibility that they would provide erroneous data.
33% of IT Leaders Believe Generative AI is Not Cracked Up to Be
Salesforce claims that one-third of IT executives believe generative AI is over-hyped. 79% of study respondents are concerned that the technology introduces new security vulnerabilities, while 73% are concerned about bias problems.
Financial Generative Artificial Intelligence Statistics
There are several statistics to consider when it comes to generative AI technology. With this in mind, we compiled a list of monetary-focused generative AI statistics that you may be interested in learning about these technologies.
Generative AI is Expected to Add Upwards of $4.4 Trillion to the Global Economy
Generative AI offers a profitable prospect. According to McKinsey’s analysis of 63 application cases, generative AI has the potential to return between $2.6 and $4.4 trillion to the global economy each year.
To put this in perspective, consider that the United States’ yearly income in fiscal year 2023 was $4.44 trillion. That is the money included via different taxes.
63% of Organizations Have Lost at Least $50 Million Due to AI/ML Governance Failures
The AI Infrastructure Alliance provides some astounding statistics on losses caused by inadequate AI/ML governance. According to their study of 1000+ enterprises with $1 billion in sales, one-third of organizations with established AI implementations suffered substantial losses when such technologies were not managed.
- 18% reported losing between $5 million and $10 million
- 19% anticipate losing $10-$50 million
- 29% reported losing between $50 million and $100 million
- 24% say they lost between $100 and $200 million
- Ten percent claim that they lost $200 + million
A Single GPU Chip May Cost $10,000 and Some Cost $40,000 +
According to CNBC, the NVIDIA A100, the industry’s popular graphics processing unit, may cost upwards of $10,000 per chip. Its replacement, NVIDIA’s H100, may cost four times as much. Corporations seldom acquire a single chip. They acquire multi-chip systems such as NVIDIA’s DGX A100 or the DGX H100, which may cost up to $47,000.
Each DGX H100 has 8 GPUs, and up to 32 DGX H100 systems may be linked together to operate up to 256 GPUs.
$700,000 is the Estimated Daily Cost of Operating ChatGPT
How much does it cost for a large generative AI supplier to run their technologies?
SemiAnalysis estimates that ChatGPT costs around $700,000 per day for hardware inference.
Generative AI tools like ChatGPT require training in addition to inference, there are costs to consider, such as large language model (LLM) training, staffing, employee, and training that add up.
Deepfake Statistics Using Generative AI
Deepfakes are here to stay. Deepfakes are pictures, audio, and video recordings created using AI technology.
Deepfake-Based Identity Fraud Doubled Between 2022 and Q1 2023
According to Sumsub research, the incidence of identity fraud in the United States increased from 0.2% to 2.6% in Q1 2023, while in Canada it increased from 0.1% to 4.6%. While this may seem to be a little sum, it will rise as technologies become accessible and utilized.
In September, three government agencies (NSA, CSI, and CISA) published a Cybersecurity Information Sheet (CSI) titled Contextualizing Deepfake Threats to Organizations. In addition to suggesting the use of passive AI detection technology and teaching personnel on what to watch out for, the authorities advise:
- We are witnessing the implementation of global standards like C2PA, as prominent manufacturers incorporate them into their devices.
- The solution is to employ public key cryptography with SSL/TLS and email encryption certificates.
- Utilizing real-time digital identity verification. This might involve implementing public key infrastructure PKI-based digital identification solutions.
Public key infrastructure, together with standards such as C2PA, enable organizations to distribute trusted information via secure and trusted channels.
96 Percent of Deepfake Videos Are Non-Consensual Pornography
DeepTrace AI identified in a 2019 research that of the 14,678 Deepfake movies uncovered online, the vast majority (96%) were classified as non-consensual pornography. According to Sensity AI’s further study, the number of deepfakes has quintupled, hitting 85,000 by December 2020. The quantity of Deepfake films online has doubled every six months.
71% of Users Are Not Aware of Deepfake Media
According to Iproov, a biometric technology vendor, seven out of ten worldwide customers are unaware of deepfakes. Less than one-third of the company’s 2022 poll respondents claim to be aware of deepfakes. The issue arises as to whether those respondents are confidence in their ability to identify a Deepfake when they encounter one.
Mexican respondents appear to think so. 82% of Mexican respondents said they could distinguish deepfakes for what they are. Germans were the least sure, with just 43% believing they could discern the difference. In a study of professionals in the US and UK, around 40% in the US and 45% in the UK said they could not tell the difference.
It is unclear if individuals can recognize deepfakes. Data from researchers at the Center for Humans and Machines, the Max Planck Institute for Human Development in Germany, and the University of Amsterdam suggest otherwise.
When Put to the Test, One-Quarter of Survey Respondents Are Unable to Identify Deepfake Audio
Did you know that one out of four individuals cannot identify a Deepfake audio sample?
According to findings from a study of 529 people, one-quarter of survey respondents were unable to distinguish between instances of Deepfake audio and actual audio recordings.
At least 25% of individuals in the United States are vulnerable to falling for deepfakes. When you consider that the population clock projection for the United States Census as of December 4, 2023 was 335,809,648 people, it equates to about 83,952,412 individuals.
30% of Indians Say at Least One Out of Four Videos See Online is Fake
A LocalCircles poll of over 32,000 Indians from 319 districts found that the country’s public is becoming aware of Deepfake media. 10,838 respondents reported discovering after the fact that 25% of the videos they viewed on their cellphones, tablets, and computers were fraudulent.
56% of poll respondents agreed that social media companies should be compelled to delete Deepfake videos of family members within 24 hours of receiving a removal request and explanation for why they should be removed.
How Can Biometric Authentication and Liveness Detection Prevent Deepfakes?
Biometric authentication is used to verify a person’s identity during an online contact, such as logging into a bank account or enrolling in a new online service. Cybercriminals are astute, and they use an ever-increasing variety of tactics to circumvent biometric authentication protection.
They may utilize images or pre-recorded films and then hold them up to a device’s camera as part of a presentation assault, or they may employ synthetic imagery that is inserted into the data stream. Researchers predict criminals to employ deepfakes in the coming years.
This is why liveness detection is critical. Liveness detection confirms that an online user is a genuine person. It employs a variety of technologies to distinguish between real individuals and fake objects. Without liveness detection, a thief might spoof a system by displaying false images, videos, or masks.
Liveness detecting methods are not equivalent. Liveness detection systems can identify a presentation assault, which involves the use of physical artifacts such as masks or recorded sessions played back to the device’s camera in an effort to spoof the system, as well as a Deepfake video held in front of the camera.
Liveness providers are unable to identify a digital injection attack, which bypasses the device’s camera and injects fake pictures into the data stream.
As our lives become digital, emergent technologies and their applications may have a significant influence on culture, legislation, and reputation. The rise of deepfakes in recent years has affected an ongoing online debate over authenticity and the distinction between reality and fiction.
When it comes to your reputation while online, impressions are crucial. In our scrolling culture, the effort required by an audience to verify the credibility of media, assuming they know how to do so, might lead to deepfakes influencing public opinion despite the fact that they may be deceiving or wrong.
However, as deepfakes become credible and popular, their reputation grows as well. Desire for authenticity and understanding of deepfakes, as well as policies that restrict their usage, may help audiences be discriminating about what they see online, therefore mitigating the negative effects of deepfakes.
FAQs – AI Deepfakes: Deepfake Statistics in 2024
What are the latest Deepfake statistics in 2024?
In 2024, the use of Deepfake technology has continued to rise. According to recent surveys, the number of Deepfake videos and audios has increased compared to 2023. Notably, the use of deepfakes for misinformation and identity fraud has been a growing concern.
How can we tell the difference between Deepfake content and real content?
As AI technology continues to advance, so does the sophistication of deepfakes. However, various companies and organizations such as Sumsub and Iproov are working on developing Deepfake detection and verification technologies to help individuals and businesses spot and verify if something is a Deepfake.
It is crucial to update Deepfake detection technologies to stay ahead of the evolving techniques used to create deepfakes.
What is the impact of deepfakes on global consumers?
Surveys show that the global consumers are concerned about the widespread use of deepfakes. The potential for AI-powered deepfakes to be used in creating misinformation, Deepfake pornography, and fake accounts has raised alarm.
The need for reliable Deepfake technology to spot a Deepfake and detection technology to respondents say if something is a Deepfake is becoming crucial than ever.
What are the most common uses of deepfakes in 2024?
Recent reports indicate the continued use of deepfakes for creating misinformation, Deepfake media, and Deepfake pornography. Additionally, there have been instances of fake accounts and forced verification using Deepfake technology, highlighting the need for improved detection technologies.