Fake Image Detection Market Size, Share, and Industry Analysis, By Solution (Photoshopped Image Detection, Deepfake Image Detection, Real-time Verification, AI-generated Image Detection, and Others (Content Authentication, Mobile Apps)), By Technology (Machine Learning & AI and Image Processing & Analysis), By Deployment (Cloud and On-premise), By Industry (BFSI, Government, Defense, IT & Telecom, Media & Entertainment, and Others (Retail & E-commerce)), and Regional Forecast, 2025-2032

Fake image detection is the process of mechanisms that recognize images or photos that have been fabricated or manipulated. Fake image detection is the operation of detecting fake images or videos that have been generated through deep learning or other mechanisms. Images are manipulated to spread disinformation online across social media sites and other websites. Hence, fake image detection technologies are used to recognize misinformation, authenticate artworks, and protect online safety.

The various kinds of fake images include deep fake images, photoshopped images, AI-generated images, and many more. Fake image detection mechanisms help authenticate images, secure online reputations, detect misinformation, and much more. The use of these fake or deepfake images adversely impacts different sectors such as journalism, politics, entertainment, finance, and more. For instance,

• According to the Sumsub Research 2023, there has been a substantial 10x rise in the number of deepfakes identified worldwide across all sectors from 2022 to 2023, with significant regional variances: 1740% deepfake growth in North America, 780% in Europe (inc. the U.K.), 1530% in Asia Pacific, 450% in the Middle East and Africa, and 410% in Latin America.

Also, the spread of misinformation, blackmail through fake image scams, and other online frauds increased in the year 2020. The counterfeit images of remedy cures forcing people to buy harmful products, images of manipulated COVID-19 treatment results, and many others. Such a rise in scams increased the demand for fake image detection tools in the market. For instance,

• An approximate 94% surge in scams and phishing activity has been recorded since 2020—prominent spikes, such as the 2.2 million fraudulent sites identified in just August alone.

The rise in such photoshopped images, deepfake images, AI-created images, and others contributes to the need for advanced solutions that can detect and prevent such threats.

Segmentation

Key Insights

The report covers the following key insights:

• Micro Macro Economic Indicators


• Drivers, Restraints, Trends, and Opportunities


• Business Strategies Adopted by the Key Players


• Consolidated SWOT Analysis of Key Players

Analysis by Industry

The market is categorized across various industries, such as BFSI, government, defense, IT & telecom, media & entertainment, and others (retail, e-commerce).

With the growing mechanisms for image manipulation, differentiating between doctored and real images can be challenging. Fake image detection with the help of tools and algorithms has become critical in fighting such issues. Fake image detection professionals constantly strive to fight with such manipulated, unseen data. As people encounter various instances of misleading visuals, disbelief becomes built across their online experiences. With the growth of modernized technology and the extensive usage of social media platforms, the distribution and manipulation of fake images have become more predominant than earlier. For instance,

• According to PeerJ’s Deepfake Forensics Report 2024, YouTube is the prominent hub, accounting for 40% of detected deepfakes. At the same time, X (Twitter) and Facebook account for 25% and 20% of fake content.

As deepfake spreads, social media enterprises are constrained to speed up fake image detection detection efforts. Video and Image bluffing has a direct influence on cybersecurity. Scam attackers have been making use of fake videos and images to trick targets and blackmail them for their money for years. For instance,

• In March 2024, the PM of Italy, Giorgia Meloni, pursued legal action and sought USD 109,345 in damages after explicit fake videos featuring her were generated and spread over the internet without her consent.

Such a rise in the number of fake images and videos across social media and other platforms drives the demand for fake image detection tools across the media and entertainment industry. 

Regional Analysis

The global fake image detection market is divided into five regions: North America, South America, Europe, the Middle East & Africa, and Asia Pacific. In 2023, North America accounted for the highest share in the fake image detection market, owing to the faster acceptance of advanced technologies, such as artificial intelligence (AI) and machine learning (ML). With the growth of these advanced technologies, online fraud, fake image scams, and deepfake attacks are also on the rise. For instance,

• According to a survey conducted by NordVPN 2023, over one in four adults in the U.S., approximately 24%, have been targeted by a romance scam. Of those beleaguered, 19% stated that they lost between USD 401 and USD 2,000 due to these romance scams. The study also discovered that 27% of adults in the U.S. have networked with a profile that turned out to be a bot or fake, while another 27% specified they got offensive images they did not demand.

Hence, there has been an increase in rules and regulations by government and regulatory bodies across the U.S. and Canada. Also, the region has a presence of prominent market players, contributing to the growth of the market in North America.

The distribution of the global fake image detection market by region of origin is as follows:

• North America – 33%


• South America – 7%


• Europe – 26%


• Middle East & Africa – 10%


• Asia Pacific – 24%

Key Players Covered

The key players in this market include Canon, OpenAI, Microsoft Corporation, DuckDuckGoose AI, Reality Defender, Sensity AI, Primeau Forensics LTD., DeepWare AI, iProov, Amped, Gradiant, Facia, iDenfy, Qualcomm, Sentinel, Deepgram, and Q-integrity.

Key Industry Developments

• In May 2024, OpenAI announced the introduction of a tool that can identify its text-to-image creator, DALL-E 3. The company stated the tool accurately identifies images generated by DALL-E 3 over 98% of the time in in-house testing. It can manage common alterations such as cropping, compression, and saturation variations with negligible impact.


• In September 2023, Google announced the launch of a tool, SynthID, that identifies AI-created images and installs invisible watermarks that are measurable by computers. This tool helps identify images created by artificial intelligence, which are becoming more and more realistic. Computers generate these AI-generated images, which can occasionally be misguided for real ones.