Fake Image Detection Market Size - By Offering (Software, Services), By Deployment Model (On-Premises, Cloud), By Organization Size (Large Enterprises, SMEs), By End User (BFSI, Government, Healthcare, Telecom, Media & Entertainment) & Forecast, 2024 - 2032

Fake Image Detection Market Size

Fake Image Detection Market size was valued at USD 800 million in 2023 and is estimated to register a CAGR of over 20% between 2024 and 2032. The proliferation of misinformation and disinformation is driving growth in the fake market. As the prevalence of fake images increases and their potential for harm is acknowledged, public awareness of the issue is growing. This has driven the demand for solutions that may help users identify between genuine and manipulated material.
 

The capacity to modify pictures may be used to change public opinion, win elections, or even incite violence.  As the potential social implications of deepfakes and other sophisticated picture forgeries become clearer, there is an increasing need to find techniques to reduce these hazards. This has encouraged governments and social advocacy groups to invest in detection technology.
 

The need to protect the brand reputation of businesses and organizations has fueled the adoption of fake image detection market. Social media platforms create an ideal environment for the proliferation of fraudulent photographs. Content may become viral in seconds, reaching a large audience before its legitimacy is validated. A single edited image may ignite a social media firestorm, destroying a brand's reputation in an instant.
 

As deepfakes and other advanced forgery tools become more widely available, the possibility of making realistic and convincing fake pictures targeting specific companies is on the rise. This emphasizes the importance of proactive detection to prevent the spread of misinformation in the first place. Furthermore, a damaged brand image might take years to recover. The negative publicity around fake photographs may persist online, discouraging potential buyers and compromising corporate collaborations, all of which has spurred the demand for increased investment in timely detection.
 

For instance, in May 2023, the New York Times reported how an AI-generated image of dense black smoke, resembling an explosion near the Pentagon, caused a brief period of fear among investors, leading to a significant stock market downturn. The unsettling image, suspected to be a fabrication likely created using artificial intelligence (AI), was swiftly debunked, highlighting the potential impact of fake imagery on financial markets and investor sentiment. This demonstrates how AI-generated fake images are used to hamper the overall reputation of any brand, company, and organization and the need to find proper detection techniques.
 

The evolving techniques of image manipulation are a major challenge for the fake image detection market, potentially slowing down its growth. The creators of fake images are constantly developing new methods to evade detection. Deepfakes, for example, use artificial intelligence to make highly lifelike forgeries that are practically undetectable from actual video. As these approaches advance, traditional detection algorithms become less effective. To keep ahead of the competition, ongoing investment in research & development is required.
 

Along with this, AI-powered detection depends largely on vast datasets of actual and altered photos to train its algorithms. However, it may be challenging to maintain these datasets up to date with the most recent alteration techniques. New forgeries may not be effectively represented in current databases, creating blind spots in detection skills.
 

Fake Image Detection Market Trends

The fake image detection industry has witnessed significant technological advancements. More advanced deep learning techniques, especially Convolutional Neural Networks (CNNs), are greatly boosting the accuracy of fake picture identification. CNNs may evaluate pictures for minute discrepancies and patterns that indicate manipulation, resulting in more accurate identification of forgeries. Advancements in data gathering and labelling techniques are resulting in richer and more diversified datasets for training AI models. These datasets provide a broader range of image types, alteration techniques, and content, allowing computers to generalize and become more robust in identifying different sorts of forgery.
 

Furthermore, the emergence of strong cloud computing platforms has enabled the processing capacity and scalability required to run large AI models efficiently. This allows for the real-time analysis of a large volume of images, making detection solutions more useful in a variety of applications.
 

For instance, in October 2023, Sumsub, a full-cycle verification platform, launched 'For Fake's Sake', a groundbreaking platform designed to detect deepfakes and synthetic fraud. This innovation enables users to estimate the likelihood of an uploaded image having been artificially created. Sumsub's in-house AI/ML Research Lab is behind the development of the platform, assembling four distinct machine learning models for deepfake and synthetic fraud detection.
 

Fake Image Detection Market Analysis

Based on offerings, the market is divided into software and services. The software segment is expected to cross over USD 3 billion by 2032. Software solutions are typically more cost-effective than service-based alternatives, since the development cost is shared by several users, making it a more attractive solution for organizations, particularly smaller and medium-sized businesses (SME/SMBs). Furthermore, software solutions are very scalable: licenses can be added on-demand, thereby helping manage costs.
 

Based on the deployment model, the fake image detection market is categorized into on-premises and cloud. The cloud segment accounted for around 70% of the market share in 2023. Cloud-based solutions are easily available from anywhere with an internet connection. Businesses do not need to invest in costly hardware infrastructure or software licensing for each user. 
 

Cloud solutions provide on-demand scalability, allowing organizations to rapidly adapt their processing and storage requirements as their needs evolve. This makes cloud solutions particularly appealing to enterprises with changing workloads. Cloud deployment removes the upfront expenditures of acquiring and maintaining hardware and software.  Cloud providers handle infrastructure and software upgrades, freeing up a company's IT staff and cutting its overall cost of ownership. 
 

North America is the fastest-growing region in the global fake image detection market with a major share of around 34% in 2023. North America is a hotspot for online material consumption, and the region is characterized by a high degree of awareness regarding the issues surrounding misinformation and disinformation attempts. This creates a huge need for solutions to detect false images.
 

Governments in North America, particularly the United States, are progressively enacting rules to fight the spread of internet misinformation. These restrictions make social media sites accountable for the content they share, prompting them to implement detection systems. Furthermore, North America is home to some of the world's most prominent technological businesses, many of which are actively creating and providing fake image detecting technologies. This makes the technology more accessible to companies in the region.
 

European countries such as France, Germany, UK, and Netherlands are also witnessing significant growth in the fake image detection market. In recent years, Europe has become the battleground for misinformation attempts. This has increased public awareness of the problem and fueled political efforts to address it. Governments are enacting legislation to hold social media sites responsible, resulting in increased demand for detection technologies. Furthermore, Europe has tougher data privacy requirements than other areas, such as the General Data Protection Regulation (GDPR).  This emphasis on privacy requires technology companies to create detection technologies that comply with these requirements. This creates a market for privacy-preserving detection techniques.
 

Across MEA region in countries such as UAE and Saudi Arabia internet and smartphone usage are rapidly growing. This expanding digital landscape creates fertile ground for the spread of fake images, fueling the need for detection solutions.
 

Fake Image Detection Market Share

In 2023, Microsoft Corporation Google, and Amazon dominated the market holding revenue share over 24%. Microsoft incorporates capabilities for detecting fake images into its Microsoft Azure cloud services, providing scalable and affordable solutions for businesses and developers to analyze, moderate, and filter images effectively.
 

Amazon provides image analysis services powered by artificial intelligence (AI) through Amazon Web Services (AWS), utilizing cloud-based machine learning features to promptly identify and flag fake images. This empowers businesses to strengthen their content moderation and safeguard their brand integrity effectively. Google maintains transparency and accountability in the fake image detection process by offering users detailed explanations and insights into the methodology behind image analysis and identification of fake images. This approach builds trust and confidence in Google's image verification technologies.
 

Fake Image Detection Market Companies

Major companies operating in the fake image detection industry are:

• Amazon
• Google
• Microsoft Corporation
• Clearview AI
• DuckDuckGoose AI
• Facia
• Ghiro AI
• Gradiant
• iDenfy
• Image Forgery Detector
• Imagga
• Intel
• Meta AI
• Q-integrity
• Sentinel AI
• Truepic
   

Fake Image Detection Industry News

• In March 2024, BioID unveiled an upgraded edition of its deepfake detection software, aimed at safeguarding biometric authentication and digital identity verification from falsified images and videos. Utilizing real-time analysis, the software combats identity spoofing by accurately identifying deepfakes and AI-manipulated content, enhancing overall security measures.
   
• In August 2023, Google launched a watermarking tool called SynthID, which is designed to detect AI-generated images and help combat deepfakes. The tool is currently available for users of Google's AI image generator, Imagen, which is hosted on Google Cloud's machine learning platform Vertex. SynthID uses two neural networks to create an embedded pattern in the image that is invisible to the human eye, and a second neural network can detect the pattern to identify whether an image has a watermark or not.
   

The fake image detection market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue (USD Billion) from 2021 to 2032, for the following segments:

Click here to Buy Section of this Report

Market, By Offering

• Software 
  • Deepfake image detection
  • Photoshopped image detection
  • AI-generated image detection
  • Real-time verification
  • Others
• Services 
  • Consulting services
  • Integration & deployment
  • Support & maintenance

Market, By Deployment Model

• On-premises
• Cloud

Market, By Organization Size

• Large enterprises
• SMEs

Market, By End User

• BFSI
• Government
• Healthcare
• Telecom
• Media & entertainment
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • ANZ
  • Southeast Asia
  • Rest of Asia Pacific 
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America 
• MEA
  • South Africa
  • UAE
  • Saudi Arabia
  • Rest of MEA