Fake Image Detection Market Size & Share Analysis - Trends, Drivers, and Forecasts (2024 - 2030)

Market Overview

The fake image detection market size is expected to grow from an estimated USD 712.2 million in 2024 to USD 5,811.9 million by 2030, with a CAGR of 41.9% during 2024–2030.

Post-production images are the kind of fake images where the pictures are manipulated, and although the face of a person may have been replaced, the pictures are so realistic that one cannot distinguish the swap, forged signatures, company names, and types of modifications.

There are countless numbers of fake image detection solution available in the Internet as the AI technology is growing and cloud computing has become prevalent. This makes them more viable and accessible to all for implementing fake image detection. This availability makes them convenient for the consumers, since they can use them time and again for various activities.

Also, these solutions proven easy to be incorporated because the selected software suite is available online and there is no need to purchase a CD and install it in the client’s PC. They also analyze data in real-time while offering a best-in-class ethically trained dataset.

The progress of the market is primarily driven by the increase in social media posts with deepfake pictures. For instance, the faces of famous politicians and celebrities as well as common bloggers were replaced with those of other individuals, and these pictures were spread all over the Internet. The most famous celebrities whose naked pictures leaked online include; Taylor Swift, Scarlett Johansson, Tom Hanks Sachin Tendulkar, Tom Cruise and Kristen Bell.

Key Insights

• Solutions are the larger category in the fake image detection market, holding a 65% share in 2024.
• Photoshopped images can easily be created by adjusting brightness, cropping, changing appearance, or making complex changes to spread misleading content.
• The rise in the popularity of Adobe Photoshop and similar software has increased the threat of false pictures.
• Several AI deepfake detector tools and software, such as DuckDuckGoose, Reality Demender, and iProov, are available online.
• Machine learning/deep learning (ML/DL) is the larger and faster-growing category, with a 70% share in 2024 and a CAGR of 42.3% during 2024–2030.
• ML/DL technologies automate the process of fake image detection, making it more accurate than human detection.
• Deepfake image detection primarily uses convolutional neural networks (CNN) and generative adversarial networks (GAN).
• The cloud category is the larger and faster-growing deployment method, with a CAGR of 42.4% during the forecast period.
• Cloud deployment offers advantages like scalability, cost-effectiveness, and anytime, anywhere accessibility.
• Cloud-based tools are easily accessible online, support multiple users, and offer a user-friendly interface.
• Additional benefits of cloud deployment include advanced security and visualization features.
• The government category holds the largest and fastest-growing market share at 35% in 2024.
• Misinformation, especially about political leaders, poses a threat to democracy and public harmony.
• Governments worldwide are evolving regulations for deepfake detection, prevention, grievance mechanisms, and awareness.
• According to Cyfrima, deepfake cases have increased by 230% recently.
• North America holds the largest market share at 45% in 2024, driven by technological advancements in deepfake tools.
• The U.S. is the largest country in this region, with initiatives like the 2021 Senate Homeland Security and Governmental Affairs Committee's Deepfakes Task Force Act.
• Asia-Pacific is the fastest-growing region in this market, with a CAGR of 42.5% during the forecast period.
• India is the fastest-growing fake image detection market due to the spread of false content related to national security, terrorism, and communal and religious sentiments. 

SOURCE: P&S Intelligence