HYDERABAD, India, Nov. 3, 2026 /PRNewswire/ — Mordor Intelligence has published a report on the deepfake AI market, providing a comprehensive assessment of market dynamics, technology trends, competitive landscape, and future opportunities. According to the study, the deepfake AI market was valued at USD 1.66 billion in 2026 and is projected to reach USD 11.76 billion by 2031, registering a CAGR of 47.93% during the forecast period (2026–2031). The study highlights how advances in generative artificial intelligence, rising demand for synthetic media, increasing deepfake-enabled financial fraud, and growing investments in detection technologies are accelerating the adoption of deepfake AI solutions across media and entertainment, banking, government, defense, and other industries.

According to industry analysis, rapid advancements in generative adversarial networks, transformer models, and multimodal artificial intelligence continue to accelerate deepfake AI market growth. Organizations are increasingly adopting deepfake artificial intelligence technologies for synthetic media creation, digital content personalization, identity verification, fraud prevention, and media authentication. At the same time, growing regulatory scrutiny and the availability of cloud-based AI services are lowering technology barriers while encouraging enterprises to invest in detection, governance, and digital forensics capabilities.

Deepfake AI Market Trends Driving Synthetic Media and Digital Authentication

Generative AI Advancements Expand Deepfake Artificial Intelligence Applications

According to industry analysis, rapid advances in generative adversarial networks are improving the quality, resolution, and coherence of synthetic media. Cloud-based AI services are making sophisticated deepfake capabilities more accessible to organizations that previously lacked the computing infrastructure required for high-resolution generation. These developments are expanding applications across film, gaming, advertising, marketing, and other creative industries while simultaneously increasing demand for technologies capable of identifying increasingly realistic synthetic content.

Financial Fraud Concerns Accelerate Deepfake Detection Adoption

Market estimates indicate that rising deepfake-enabled financial fraud is encouraging banks and financial institutions to strengthen identity verification and authentication workflows. Multimodal detection technologies are gaining importance across customer onboarding, contact centers, mobile banking, and eKYC processes as organizations seek to identify manipulated voice, video, and image content. The growing use of deepfake artificial intelligence for both content generation and fraud prevention is creating opportunities for technology providers across financial services, government, and cybersecurity applications.

Cloud Deployment Expands Access to Deepfake AI Technology

Organizations are increasingly adopting cloud-based deepfake AI solutions because they provide scalable computing resources, managed infrastructure, and flexible deployment models. The growing availability of cloud-based APIs is also lowering entry barriers for mid-sized organizations and creative businesses seeking to integrate deepfake artificial intelligence into existing workflows.

Ashish Gautam, Senior Research Manager, Mordor Intelligence says, “Organizations and technology providers evaluating opportunities in the deepfake AI market need research that clearly separates the expanding applications of synthetic media from the growing requirements for detection, authentication, and responsible AI governance. Mordor Intelligence combines extensive primary research with rigorous secondary analysis to provide balanced, evidence-based insights that support informed business decisions across rapidly evolving artificial intelligence markets.”

Check out more details and stay updated with the latest industry trends, including the Japanese version for localized insights: https://www.mordorintelligence.com/ja/industry-reports/deepfake-ai-market?utm_source=prnewswire

Geographic Analysis of the Deepfake AI Market

North America continues to lead the deepfake AI market, supported by established artificial intelligence ecosystems, enterprise adoption across entertainment and financial services, government investment, and growing demand for digital authentication. Continued investment in cloud infrastructure, cybersecurity, and anti-fraud technologies is expected to support further market development. The United States remains a major market for both synthetic-media applications and deepfake detection solutions.

Asia Pacific is expected to experience strong growth in the deepfake artificial intelligence market, supported by government-backed AI investments, expanding digital infrastructure, increasing adoption of edge computing, and a growing ecosystem of deepfake technology providers. China, India, Japan, South Korea, and other technology-focused economies are creating opportunities for synthetic-media generation, detection, authentication, and responsible AI applications.

Table of Contents (Partial) – Deepfake AI Market

INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the StudyRESEARCH METHODOLOGYEXECUTIVE SUMMARYMARKET LANDSCAPE
4.1 Market Overview
4.2 Market Drivers
4.2.1 Rapid Advancements in Generative Adversarial Networks
4.2.2 Rising Demand for Personalized Synthetic Media in Marketing and Entertainment
4.2.3 Surge in Deepfake-Enabled Financial Fraud Driving Investment in Detection Solutions
4.2.4 Government Procurement of Deepfake Detection for Defense and Law-Enforcement Operations
4.2.5 Integration of Multimodal Deepfake Forensics into eKYC Pipelines
4.2.6 Availability of Large Open-Source Multimodal Deepfake Datasets
4.3 Market Restraints
4.3.1 Regulatory Uncertainty and Fragmented Legal Frameworks
4.3.2 High Computational Costs for Real-Time High-Resolution Deepfake Generation and Detection
4.3.3 Erosion of Consumer Trust Reducing Monetization of Synthetic Media Platforms
4.3.4 Shortage of Diverse Training Data Causing Detection BiasMARKET SIZE AND GROWTH FORECASTS (VALUE)
5.1 By Offering
5.1.1 Software
5.1.2 Services
5.2 By Technology
5.2.1 Generative Adversarial Networks
5.2.2 Transformer Models
5.2.3 Autoencoders
5.2.4 Other Technologies
5.3 By Application
5.3.1 Media and Entertainment
5.3.2 Banking, Financial Services and Insurance
5.3.3 Government and Defense
5.3.4 Healthcare and Life Sciences
5.3.5 Advertising and Marketing
5.4 By Deployment Mode
5.4.1 Cloud
5.4.2 On-Premises
5.5 By Geography
5.5.1 North America
5.5.2 South America
5.5.3 Europe
5.5.4 Asia Pacific
5.5.5 Middle East
5.5.6 AfricaCOMPETITIVE LANDSCAPE
6.1 Market Concentration
6.2 Strategic Moves
6.3 Market Share Analysis
6.4 Company Profiles
6.4.1 Synthesia Limited
6.4.2 D-ID Ltd.
6.4.3 Sentinel Labs OU
6.4.4 Reality Defender Inc.
6.4.5 Reface AI Ltd.
6.4.6 Microsoft Corp.
6.4.7 Google LLC
6.4.8 Amazon Web Services Inc.
6.4.9 NVIDIA Corporation
6.4.10 and moreMARKET OPPORTUNITIES AND FUTURE OUTLOOK
7.1 White-Space and Unmet-Need Assessment

For details on other market segments and the full table of contents, visit:

https://www.mordorintelligence.com/industry-reports/deepfake-ai-market?utm_source=prnewswire

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About Mordor Intelligence

Mordor Intelligence is a global market intelligence firm, founded in 2014, helping businesses make informed decisions through data-driven insights, industry expertise, and advanced analytics. With 550+ domain experts and on-ground specialists across 180+ countries, the company delivers comprehensive intelligence across aerospace & defense, agriculture, automation, automotive, chemicals & materials, consumer goods & services, electronics, energy & power, financial services, food & beverages, healthcare, hospitality & tourism, information & communications technology, and logistics.

Its offerings include syndicated industry reports, custom research and advisory, and subscription platforms such as Synapse, Data Center Intelligence, and foldable smartphone Intelligence. Mordor Intelligence is ISO 9001:2015 and ISO/IEC 27001:2022 certified, a member of ESOMAR and the Market Research Society of India, and a four-time Great Place to Work® honoree. Learn more at mordorintelligence.com.

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SOURCE Mordor Intelligence Private Limited

MFour links each AI conversation to what the same consumer does next, online and in stores, for full journey intelligence.

IRVINE, Calif., Nov. 3, 2026 /PRNewswire/ — MFour Data Research, Inc., provider of the largest mobile-app consumer panel in the U.S. and the only platform connecting surveys and behaviors for full journey intelligence from ChatGPT to checkout, has added Google Gemini. With Gemini joining ChatGPT, MFour now collects 2.35 million unique AI conversations every 30 days, containing 10.1 million consumer-generated messages.

Brands and agencies can access this dataset of real human prompts and responses in two ways: by licensing it directly or by asking DANI™, MFour’s AI-powered analyst, to produce boardroom-ready reports at whatever depth the research requires. DANI can run autonomously for hours, sifting through billions of signals to surface the insights that matter and recommend next steps, taking researchers from question to outcome with a single prompt.

To uncover the motivations behind the behavior, DANI can even survey the same consumers who contributed the data, connecting what consumers do with why they do it in a single workflow.

Each LLM conversation is linked to the same consumer’s demographics, web browsing, app usage, GPS-verified store visits, purchases and survey responses. This gives brands a fuller view of how consumers research products and make decisions, including what they do before and after asking AI for advice.

The Gemini data is captured at the account level, covering conversations in the Gemini app on any mobile device as well as in any desktop browser.

“AI is changing where brands win or lose consideration. If you can’t see those conversations, you’re missing a critical part of the buying decision,” said Chris St. Hilaire, CEO of MFour Data Research. “Adding Gemini expands that visibility beyond ChatGPT, revealing how consumers interact with AI and what they do next online or in-store.”

All of this data comes from MFour’s first-party panel, whose members opt in to share behavioral data across the entire path to purchase. Under MFour’s ethical, fair-trade practices, panelists are compensated both for sharing their data and for taking surveys.

About MFour Data Research

MFour Data Research, founded in 2011, provides validated, opt-in consumer behavior and survey data powered by its Surveys On The Go® app and Fair Trade Data® model. The company’s 14M+ consumer panel generates nine deterministic data streams — AI conversations across ChatGPT and Gemini, location, app usage, web browsing, receipts, demographics, attitudes, survey responses and behavior-triggered surveys — all connected to a single identity.

Through MFour Studio™, organizations can access these behavioral and survey signals to understand consumer journeys and measure outcomes, with DANI™ (Data Analytics Navigation Instructor), MFour’s AI assistant, providing on-demand analysis across 4 billion monthly buyer signals. MFour is trusted by Samsung, Google, Amazon Ads, Microsoft, Hulu, Keurig Dr Pepper, Papa Johns and hundreds of other global brands.

Full Journey Intelligence. Know What to Do Next.

Learn more at mfour.com

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SOURCE MFour Data Research

HONG KONG, Nov. 3, 2026 /PRNewswire/ — Alex Fang, Co-founder and Chief Financial Officer of Astribot, an embodied AI company, attended the China-Global Innovation Forum during Hong Kong FinTech Week x StartmeupHK 2026, alongside industry and investment representatives from Kinetix AI, KEENON Robotics and Yunqi Partners. Under the theme “From Components to Systems: Identifying the Primary Bottleneck in Robotics Industrialization,” they jointly explored the core bottlenecks in robotics industrialization, pathways to large-scale deployment, and sustainable business models.

As AI moves beyond the digital world into physical environments, the embodied AI industry is reaching a pivotal stage in its transition from technological demonstrations to commercial deployment at scale. The industry’s next test is whether robotics companies can deliver products consistently in volume, secure repeat business and balance heavy research and development spending with commercial returns.

At the forum, Fang said Astribot has prioritized product development and commercialization since its founding. The company has moved through successive stages of technological demonstrations, small-batch manufacturing and repeat orders, while making progress on hardware gross margins for certain products. Looking ahead, Astribot will focus on scaling its operations, improving cost efficiency, strengthening delivery capabilities and accelerating technological development.

Thousand-Unit Orders Across Multiple Applications Put Commercial Viability to the Test

According to Fang, Astribot has secured multiple repeat orders from developer and commercial service customers since beginning mass production in 2025. Delivery volumes for certain customers have also grown steadily in specific applications. Meanwhile, the company is exploring paid software offerings as a way to generate additional revenue beyond hardware sales.

Fang noted that higher delivery volumes could help the company reduce supply chain costs, improve manufacturing consistency and better manage after-sales expenses. Applying the same underlying technologies across different use cases could also support stronger gross margins.

From the outset, Astribot has sought to position robots as a gateway to Physical AI. By developing its robotic hardware, operating system and AI models together, the company aims to make it easier for users and developers to build a wider range of applications. As deployments expand, Astribot plans to draw on operational data and experience from real-world settings to refine its products and AI models, laying the groundwork for broader adoption.

Astribot previously disclosed that its flagship S-series robots began securing orders at the thousand-unit scale in September 2025. Shipments of its next-generation T1 humanoid robot began in June 2026, with prices starting at RMB 89,900. The company has also stated that its S and T series have a combined production capacity of around 10,000 units, providing a manufacturing base for further expansion.

Astribot’s products are already being used across retail, tourism and cultural services, industrial operations, research and education. Its in-house-developed Robot MART has been rolled out in six Chinese cities, including Beijing, Shanghai and Guangzhou, since November 2025, as the company tests the operational and commercial viability of robot-powered retail services. Astribot is also working with partners including Jinma Rides, SEER Robotics and ThunderSoft to bring its technologies into commercial and industrial environments.

Beyond commercial services, Astribot is building a developer ecosystem around standardized robotic platforms, skill modules and development tools. These resources are designed to help research institutions and AI companies deploy models and develop new applications. Fang explained that, particularly in industrial settings, Astribot focuses on supplying the underlying platforms and technical capabilities, while partners customize the platforms and deploy them for specific applications. This approach allows partners to develop their own applications and create opportunities for value-added services.

Beyond Cable-Driven Robotics: Building a Competitive Edge Through a Full-Stack AI Architecture

Astribot describes itself as the world’s first company to have achieved mass production of cable-driven AI robots. Its approach uses flexible cables to transmit mechanical power, with cost efficiency and reliability among the key advantages the company attributes to the technology.

Fang explained that Astribot’s choice of cable-driven robotics was guided by the requirements of AI. The company considered several factors, including the ability to capture feedback from interactions with the physical environment, safety during human-robot contact and the cost of hardware.

For robots to become a practical gateway to Physical AI, Fang said, the hardware must be both affordable and easy to use. The operating platform must allow users to control and train robots with relative ease, while the AI models must be capable of handling a diverse range of tasks. Together, these capabilities would enable software and applications to generate greater value over time.

Addressing cost and durability, Fang said cable-driven systems can reduce reliance on components such as gear reducers. He estimated that Astribot’s current robot production costs could be roughly one-third of those of industry peers. On reliability, Fang said third-party service-life testing had indicated that the company’s robots could operate reliably for more than six years, while after-sales expenses for products already delivered have remained relatively low.

This AI-first approach underpins Astribot’s broader research and development strategy. Guided by its “Design for AI” philosophy, the company has built an integrated, full-stack architecture combining three core elements: its Lumo embodied foundation model, Astribot OS, and a cable-driven robotic body.

By integrating AI models, an embodied operating system and physical hardware, the architecture creates a continuous feedback loop between the robot, real-world data and AI models.

Each layer serves a distinct function. The cable-driven robotic body enables flexible, precise physical manipulation. The Lumo foundation model supports environmental understanding, task reasoning and action generation. Astribot OS brings these capabilities together into applications that users and developers can access and deploy.

Astribot expanded its AI capabilities in July 2026 with the introduction of Lumo-2, its embodied foundation model. This was followed in September by SmoothRL, an online reinforcement learning framework designed to help robots refine their behavior as they perform tasks and adapt more effectively to changes in real-world environments.

Fresh Funding Supports Astribot’s Next Stage of Commercial Expansion

As Astribot scales product deliveries and expands commercial applications, it is stepping up investment in research and development, talent acquisition and manufacturing capacity.

In October 2026, Astribot completed a new RMB 1 billion funding round, bringing its valuation to RMB 15 billion. The financing marked Astribot’s fourth funding round since March 2026, providing additional capital to support technological innovation and the expansion of its commercial operations.

The round was led by Shenzhen Toposcend Capital, an affiliate of Shenzhen Investment Holdings, with participation from Hongtai Aplus, the Guanghua Sunshine-managed Ninghai Xingtaihe Fund, Soliton Capital and other investors. Existing investor Xianmu also participated in the round.

The proceeds are intended primarily for further AI model development, recruitment, commercial deployment and manufacturing capacity expansion, supporting the company’s efforts to strengthen product development and deliver at scale.

Astribot’s participation in Hong Kong FinTech Week also provided a platform for dialogue with international technology companies, investors and other industry stakeholders. This year’s event highlights three engines of growth — Finance+, AI+ and Global Expansion — bringing together businesses and investment institutions from markets around the world.

With its international financial networks and strong cross-border business connections, Hong Kong offers Chinese technology companies a gateway to global resources and opportunities to develop partnerships in overseas markets.

Looking ahead, Astribot plans to continue pursuing commercialization through two complementary paths: commercial services and its developer ecosystem.

Fang said the company aims to further increase product deliveries in 2027 while exploring the introduction of AI robots designed for individual consumers, gradually expanding into household applications.

As Astribot’s robots enter more real-world environments, the company intends to improve their manipulation and human-robot interaction capabilities further, supporting the broader transition of embodied AI from specialized applications into everyday life.

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SOURCE Astribot