GVR Report cover Fake Image Detection Market Size, Share & Trends Report

Fake Image Detection Market Size, Share & Trends Analysis Report By Offering (Software, Services), By Deployment (On Premises, Cloud), By Technology, By Vertical, By Region, And Segment Forecasts, 2024 - 2030

  • Report ID: GVR-4-68040-274-2
  • Number of Pages: 150
  • Format: Electronic (PDF)
  • Historical Range: 2017 - 2022
  • Industry: Technology

Fake Image Detection Market Size & Trends

The global fake image detection market size was estimated at USD 818.5 million in 2023 and is projected to grow at a CAGR of 37.8% from 2024 to 2030. Fake image detection has emerged as a critical technology in response to the proliferation of manipulated visual content, driven largely by advancements in artificial intelligence (AI) and deep learning algorithms. Globally, the demand for image detection solutions has seen a significant growth, spurred by the rising concerns over the spread of misinformation, fraud, and privacy breaches. The media and entertainment industry has witnessed a surge in the adoption of image detection technologies to combat the spread of deepfakes and manipulated images, safeguarding the integrity of digital content, and preserving trust among audiences.

Global Fake Image Detection Market size and growth rate, 2024 - 2030

Moreover, sectors, such as government, finance, healthcare, and e-commerce, are increasingly turning to image detection to mitigate risks associated with fraudulent activities, ensure data integrity, and uphold security protocols. With continued advancements in detection techniques and an ever-evolving landscape of digital threats, the global market for fake image detection is poised for continued growth, with an emphasis on innovation and collaboration across industries to stay ahead of emerging challenges. The growing complexity of both malicious actors and detection technologies are fueling the market growth.

As deepfake technology becomes more accessible and realistic, the arms race between creators of fake content and developers of detection tools intensifies. This has led to an increased focus on multi-modal detection approaches, combining techniques from computer vision, natural language processing, and forensic analysis to identify subtle inconsistencies indicative of manipulation. Moreover, there's a rising emphasis on explainability and interpretability in image detection systems, enabling users to understand how decisions are made and increasing trust in the reliability of the technology.

Moreover, the regulatory landscape surrounding fake image detection is evolving, with governments and industry bodies exploring measures to address the ethical, legal, and societal implications of manipulated media. Collaborative efforts between researchers, technology companies, and policymakers drive innovation, fostering the development of robust, scalable solutions to combat the spread of fake images across various online platforms. Overall, the trajectory of fake image detection reflects a dynamic ecosystem characterized by continual adaptation and refinement in response to emerging threats and societal demands for transparency and accountability.

Market Concentration & Characteristics

Recent advancements in fake image detection have been characterized by innovative approaches leveraging machine learning (ML), computer vision, and forensic techniques. One notable trend is the development of deep learning models specifically tailored for detecting manipulated images, including deepfake videos and AI-generated imagery. These models are trained on large datasets of authentic and manipulated images to learn subtle patterns and anomalies indicative of tampering. The fake image detection industry is changing due to a large amount of merger and acquisition (M&A) activities.

Fake Image Detection Market Concentration & Characteristics

To increase their market share and improve service offerings, larger firms deliberately buy out smaller platforms or startups or establish partnerships with them. For instance, in June 2023, iDenfy, a leading RegTech startup providing AI-driven identity verification and fraud prevention solutions on a global scale, has unveiled its collaboration with LeakIX, a cybersecurity platform specializing in the aggregation and analysis of internet data. This partnership aims to assist organizations in mitigating security threats more effectively. By integrating iDenfy's cutting-edge ID verification solution, LeakIX seeks to bolster its capabilities in detecting payment fraud and thwarting the creation of counterfeit accounts, thereby enhancing the overall security posture of its services.

The regulations can significantly boost the industry growth by mandating its use in key verticals and promoting user trust. In addition, regulations might establish standards for detection, improving overall accuracy. However, overly strict rules could stifle innovation and burden companies with compliance costs. Overall, regulations are likely to benefit the market, but a balance between effectiveness and fostering a dynamic industry is crucial.

The market caters to a wide range of users. Media and entertainment companies, currently the biggest users, need this tech to verify the authenticity of visuals. Governments are expected to be the fastest-growing segment due to their fight against misinformation. The banking, financial services, and insurance industries are also likely to adopt this tech to combat fraud. The healthcare, defense, and other sectors are also using it to protect patient data and national security. With the advent of sophisticated image editing tools and AI-powered deepfake technology, the industry faces significant challenges in ensuring the authenticity and integrity of visual assets used in advertising campaigns, promotional materials, and media productions.

Offering Insights

The software segment led the market and accounted for 51.6% of the global revenue in 2023. Software solutions are gaining popularity for their cost-effectiveness and scalability, as development expenses are distributed among multiple users, making them particularly appealing for small and medium-sized enterprises (SMEs). Additionally, organizations easily add software licenses as needed, enabling efficient cost management, and fueling the market expansion. Moreover, Sophisticated deep learning methods, notably Convolutional Neural Networks (CNNs), are enhancing the precision of fake image detection software. By scrutinizing images for subtle irregularities and discernible patterns suggestive of manipulation, CNNs facilitate more precise detection of counterfeit images.

The services sector within the fake image detection industry is experiencing significant growth, driven by the increasing demand for specialized expertise, seamless integration of detection solutions, and ongoing support to ensure the effectiveness and reliability of these technologies.Consulting services play a crucial role in guiding organizations through the complex landscape of fake image detection. Consultants provide strategic advice on selecting the most suitable detection solutions based on the specific needs and requirements of clients. They offer expertise in evaluating different technologies, understanding regulatory compliance, and developing tailored strategies for implementation. With the growing awareness of the importance of fake image detection, the demand for consulting services is on the rise, especially among industries facing heightened security and privacy concerns.

Deployment Insights

The cloud segment accounted for the highest share in 2023. Cloud infrastructure offers access to extensive computational resources, facilitating accelerated processing and analysis of substantial image datasets. This scalability proves essential for promptly detecting fake images in real time across diverse online platforms and applications. Cloud service providers are frequently offering sophisticated ML algorithms and AI frameworks, elevating the precision and effectiveness of fake image detection models. These frameworks utilize methodologies, such as deep learning and convolutional neural networks, to pinpoint subtle irregularities and patterns suggestive of manipulation.

On-premises deployment of fake image detection solutions involves hosting and running the detection software within the organization’s own infrastructure, rather than relying on cloud-based services or external providers. One of the primary advantages of on-premises deployment is the ability to maintain full control over sensitive data. Organizations can ensure that all image data remains within their own secure network, reducing the risk of data breaches or unauthorized access. This level of control is especially critical for industries with strict regulatory requirements or heightened security concerns, such as government agencies or financial institutions.

Technology Insights

The ML and AI segment held the highest revenue share in 2023. ML and AI are revolutionizing the industry by enabling the development of sophisticated algorithms capable of identifying manipulated visual content with unprecedented accuracy and efficiency. ML and AI techniques, such as deep learning, convolutional neural networks (CNNs), and generative adversarial networks (GANs), are at the forefront of fake image detection research. These algorithms can analyze large datasets of authentic and manipulated images to learn complex patterns and features indicative of tampering. By leveraging deep learning architectures, detection models can identify subtle inconsistencies and artifacts in images that may be imperceptible to the human eye, leading to more reliable detection results.

Image processing and analysis play a crucial role in fake image detection by providing the foundational techniques and methodologies for identifying manipulated visual content. Image preprocessing techniques, such as noise reduction, contrast enhancement, and image normalization, are used to improve the quality and consistency of input images before analysis. Preprocessing helps standardize image characteristics and remove irrelevant information, making it easier for detection algorithms to identify subtle anomalies and inconsistencies indicative of manipulation. Moreover, Image analysis techniques enable the recognition of patterns and structures within images that are characteristic of different types of manipulation. Pattern recognition algorithms, such as template matching, statistical analysis, and ML classifiers, are trained to identify patterns associated with common forms of manipulation, such as splicing, cloning, or retouching.

Vertical Insights

The defense segment accounted for a significant revenue share in 2023. Prominent innovation and growth in forensics and security is augmenting the market expansion. Forensics and security in defense are pivotal in countering the widespread dissemination of counterfeit images, which pose significant risks across multiple sectors. Image forensics stands as a crucial instrument in criminal inquiries, furnishing pivotal evidence in matters concerning fraud, defamation, or exploitation. Through the identification of altered images, forensic specialists can unveil vital evidence, affirm the genuineness of visual materials, and bolster legal proceedings.

Global Fake Image Detection Market share and size, 2023

Retail & e-commerce is also significantly propelling the market expansion. Major e-commerce companies like Amazon and Alibaba are leveraging AI-powered image analysis to identify counterfeit products listed on their platforms. By comparing product images against databases of authentic items, they detect even minor inconsistencies that may indicate a counterfeit product, protecting both consumers and brands from fraudulent sales. Moreover, luxury and high-end brands are particularly vulnerable to counterfeiting, which can severely damage their reputation and customer trust. Companies like GOAT and Entrupy use fake image detection algorithms to authenticate luxury goods before reselling them, ensuring that only genuine products reach customers.

Regional Insights

The North America fake image detection market dominated the market and accounted for a share of 32.60% in 2023. North America is a hub for technological innovation, with many companies and research institutions leading the development of advanced fake image detection technologies. Leading tech companies, startups, and academic institutions in the region are actively engaged in R&D efforts aimed at enhancing the accuracy, efficiency, and scalability of fake image detection algorithms. In recent years, there has been growing regulatory scrutiny surrounding the spread of fake images and misinformation online. Government agencies, regulatory bodies, and industry organizations in North America are focusing on addressing the societal, ethical, and legal implications of manipulated visual content. This has led to an increased investment in fake image detection technologies and initiatives aimed at combating the spread of fake images and ensuring digital trust and integrity.

Fake Image Detection Market Trends by Region, 2024 - 2030

U.S. Fake Image Detection Market Trends

The fake image detection market in the U.S. is witnessing significant growth and adoption, driven by the presence of major technology companies, increasing awareness of misinformation threats, and robust investment in advanced solutions. Moreover, the presence of major tech giants like Microsoft, Google, and Amazon that are investing heavily in developing cutting-edge fake image detection technologies is supporting the domestic market growth.

Europe Fake Image Detection Market Trends

Europe is increasingly recognizing the significance of countering the spread of fake images across digital platforms. Collaborative efforts among governments, tech companies, and civil society organizations have been initiated to enact measures focused on detecting and mitigating the dissemination of manipulated or falsified images.

The fake image detection market in the UK is expected to witness significant growth over the forecast years. Fake image detection has emerged as a critical aspect of combating the spread of misinformation and ensuring the integrity of digital content. With the rise of deepfake technology and the proliferation of manipulated images on social media and other online platforms, there is a growing recognition of the need for robust detection mechanisms.

The France fake image detection market is projected to witness considerable growth over the forecast period. Fake image detection has accumulated significant attention as a crucial tool in combating the spread of misinformation and preserving the authenticity of digital content. With the increasing prevalence of manipulated images across various online platforms, there is a growing emphasis on the development and deployment of advanced detection technologies.

The fake image detection market in Germany will witness significant growth in the future. The advancement of fake image detection technology is witnessing notable trends, driven by the urgent need to counter the proliferation of manipulated visual content. With the emergence of increasingly sophisticated deepfake techniques and the rising prevalence of manipulated images across digital platforms, there is a heightened focus on developing innovative detection solutions.

Asia Pacific Fake Image Detection Market Trends

The Asia Pacific fake image detection market is anticipated to register the fastest CAGR from 2024 to 203. The regional market is experiencing notable expansion and acceptance, propelled by a rising recognition of the risks posed by misinformation and the imperative to counter manipulated images across diverse sectors. This region is actively driving technological progress, marked by continuous exploration and innovation in fields, such as deep learning methodologies, transfer learning, ensemble models, and adversarial training techniques. Nations like Japan, South Korea, and Singapore are leading these advancements, spurred by robust research endeavors and fruitful collaborations between academic institutions and industry players.

The fake image detection market in China is poised to emerge as a rapidly expanding market, propelled by its extensive user base, pervasive integration of digital technologies, and growing recognition of the dangers posed by misinformation.

The India fake image detection market is anticipated to become one of the swiftly advancing markets buoyed by the nation's expansive user base, extensive uptake of digital technologies, and escalating apprehensions regarding fake news and misinformation. Moreover, The Indian government has implemented measures to tackle the challenge of fake news and misinformation. For instance, the updated Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules of 2021 mandate social media platforms to promptly remove false or deceptive content upon receipt of a court order or government notification.

The fake image detection market in Japan is experiencing increased collaboration between industry, academia, and government to develop innovative detection technologies. With a growing awareness of the risks posed by manipulated visual content, there is a concerted effort to leverage Japan's expertise in AI and computer vision to advance detection capabilities.

Middle East & Africa (MEA) Fake Image Detection Market Trends

The Middle East & Africa fake image detection market is projected to experience rapid growth in the coming years. In MEA, there is a growing recognition of the importance of fake image detection in combating misinformation and preserving digital integrity. With the rapid expansion of digital technologies and online platforms across the region, there is an increasing need for robust detection mechanisms to identify and mitigate the spread of manipulated visual content. Governments, tech companies, and civil society organizations are working together to develop and implement solutions tailored to the unique challenges and cultural contexts of the MEA region.

Key Fake Image Detection Company Insights

Major corporations are leveraging a mix of strategic initiatives to expand their market reach. These tactics include expansions, product launches, partnerships, mergers and acquisitions, and collaborations. This multifaceted approach allows them to gain market share and solidify their position within competitive industries. In March 2024, BioID unveiled an updated version of its deepfake detection software, bolstering biometric authentication and digital identity verification systems. This advanced solution swiftly identifies manipulated images and videos in real-time, offering instant analysis and feedback for both photos and videos. As a result, it effectively thwarts identity theft endeavors that leverage deepfakes or AI-generated content.

Key Fake Image Detection Companies:

The following are the leading companies in the fake image detection market. These companies collectively hold the largest market share and dictate industry trends.

  • Amped
  • Canon
  • Deepgram
  • DeepWare AI
  • Gradiant
  • Intel
  • Microsoft Corp.
  • Qualcomm
  • Sensity AI
  • Sentinel
  • Sony Corp.

Recent Developments

  • In June 2023, iDenfy, a firm focusing on ID verification services, collaborated with LeakIX, a cybersecurity platform specializing in web data analysis, to enhance the latter's defenses against payment fraud and fake account creation. Through this partnership, iDenfy's verification solution will be integrated into LeakIX's systems. In essence, LeakIX will utilize iDenfy's technology to improve user identity verification and thwart fraudulent activities effectively

  • In August 2022, Microsoft Corp. introduced its Video Authenticator software, offering users the ability to discern both deepfake photos and videos. This pioneering tool employs a confidence score system, furnishing users with valuable data to evaluate the authenticity of the media they come across

Fake Image Detection Market Report Scope

Report Attribute


Market size value in 2024

USD 1,069.7 million

Revenue forecast in 2030

USD 7.32 billion

Growth rate

CAGR of 37.8% from 2024 to 2030

Base year for estimation


Historical data

2017 - 2022

Forecast period

2024 - 2030

Quantitative units

Revenue in USD million/billion and CAGR from 2024 to 2030

Report coverage

Revenue forecast, company ranking, competitive landscape, growth factors, and trends

Segments covered

Offering, deployment, technology, vertical, region

Regional scope

North America; Europe; Asia Pacific; Latin America; MEA

Country scope

U.S.; Canada; Mexico; UK; Germany; France; China; Japan; India; South Korea; Australia; Brazil; UAE; Saudi Arabia (KSA); South Africa

Key companies profiled

Amped; Canon; Deepgram; DeepWare AI; Gradiant; Intel Corp.; Microsoft Corp.; Qualcomm; Sensity AI; Sentinel; Sony Corp.

Customization scope

Free report customization (equivalent up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope

Pricing and purchase options

Avail customized purchase options to meet your exact research needs. Explore purchase options


Global Fake Image Detection Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global fake image detection market report based on offering, deployment, technology, vertical, and region:

  • Offerings Outlook (Revenue, USD Million, 2017 - 2030)

    • Software

      • Deepfake Image Detection

      • Photoshopped Image Detection

      • AI-generated Image Detection

      • Real-time Verification

      • Others

    • Services

      • Consulting Services

      • Integration & Deployment

      • Support & Maintenance

  • Deployment Outlook (Revenue, USD Million, 2017 - 2030)

    • On-premises

    • Cloud

  • Technology Outlook (Revenue, USD Million, 2017 - 2030)

    • Image Processing & Analysis

    • Machine Learning & AI

  • Vertical Outlook (Revenue, USD Million, 2017 - 2030)

    • Government

    • BFSI

    • Healthcare

    • IT & Telecom

    • Defense

    • Media & Entertainment

    • Retail & E-commerce

    • Others

  • Regional Outlook (Revenue, USD Million, 2017 - 2030)

    • North America

      • U.S.

      • Canada

      • Mexico

    • Europe

      • Germany

      • UK

      • France

    • Asia Pacific

      • China

      • Japan

      • India

      • South Korea

      • Australia

    • Latin America

      • Brazil

    • Middle East and Africa (MEA)

      • UAE

      • KSA

      • South Africa

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