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市场调查报告书
商品编码
1513567

假影像侦测市场规模 - 按产品、部署模型、组织规模、最终用户和预测，2024 年至 2032 年

Fake Image Detection Market Size - By Offering, By Deployment Model, By Organization Size, By End User & Forecast, 2024 - 2032
出版日期: | 出版商: Global Market Insights Inc. | 英文 250 Pages | 商品交期: 2-3个工作天内

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简介目录

在人工智慧和机器学习技术创新的推动下，2024 年至 2032 年间，全球虚假影像侦测市场规模将达到 20% 的复合年增长率。在数位媒体的普遍影响力的推动下，操纵视觉效果的案例不断增加，对先进检测工具的需求也随之增加。这些创新使企业、政府和线上平台能够维护诚信、打击欺骗并维护公众对数位内容的信任。这一趋势凸显了向主动措施的重大转变，以识别和减轻各个部门和社会环境中虚假图像的影响。

例如，2024 年 5 月，OpenAI 推出了一款工具来检测人工智慧生成的图像，标记和保护数位内容以打击错误讯息，特别是在选举等关键事件期间。这一发展表明，对能够有效识别和防范人工智慧生成图像的技术的需求不断增加，特别是在选举等敏感时期。它突显了市场的潜在扩张，因为组织寻求先进的工具来对抗操纵视觉效果的扩散并保持对数位内容完整性的信任。

虚假影像检测产业根据产品、部署模型、组织规模、最终用户和区域进行细分。

到 2032 年，大型企业部门将在先进技术和强大的网路安全措施方面利用大量资源，建立相当大的立足点。这些企业面临恶意行为者传播错误讯息的更大风险。对人工智慧和机器学习解决方案的投资使他们能够有效地检测和减少虚假图像。此外，合规性要求和声誉管理推动了复杂检测工具的采用。作为品牌诚信和公众信任的守护者，大型企业在塑造假影像检测技术不断发展的格局方面发挥关键作用。

由于该行业极易遭受诈欺和声誉风险，BFSI 细分市场到 2032 年将获得显着收益。金融机构越来越依赖先进的人工智慧和机器学习演算法来检测身分盗窃和伪造文件等诈欺活动中使用的操纵影像。监管合规要求和客户信任维护进一步推动了采用。随着金融交易日益线上化，BFSI 领域在提高假影像检测技术的有效性和采用方面发挥关键作用。

由于数位化的快速发展、网路普及率的提高以及错误讯息的增加，亚太地区虚假影像检测市场份额从 2024 年到 2032 年将实现显着的复合年增长率。该地区的政府和企业正在投资人工智慧驱动的技术来打击虚假图像。此外，大型科技公司的存在和新兴的新创生态系统有助于亚太地区成为全球假影像检测产业的重要贡献者。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

  • 产业生态系统分析
  • 供应商格局
    • 数据提供者
    • 技术开发商
    • 软体供应商
    • 系统整合商
    • 云端服务供应商
  • 利润率分析
  • 技术与创新格局
  • 专利分析
  • 重要新闻和倡议
  • 监管环境
  • 衝击力
    • 成长动力
      • 错误讯息和虚假讯息的扩散
      • 人工智慧 (AI) 和机器学习 (ML) 的进步
      • 保护企业和组织的品牌声誉
      • 政府监管合规性以规范虚假图像的使用
    • 产业陷阱与挑战
      • 不断发展的影像处理技术
      • 影像资料量大且多样化
  • 成长潜力分析
  • 波特的分析
  • PESTEL分析

第 4 章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第 5 章：市场估计与预测：依产品分类，2021 - 2032 年

  • 主要趋势
  • 软体
    • Deepfake影像侦测
    • Photoshop 影像侦测
    • AI产生的影像侦测
    • 即时验证
    • 其他的
  • 服务
    • 咨询服务
    • 整合与部署
    • 支援与维护

第 6 章：市场估计与预测：按部署模型，2021 - 2032 年

  • 主要趋势
  • 本地
  • 云

第 7 章：市场估计与预测：依组织规模，2021 - 2032 年

  • 主要趋势
  • 大型企业
  • 中小企业

第 8 章：市场估计与预测：按最终用户划分，2021 - 2032 年

  • 主要趋势
  • BFSI
  • 政府
  • 卫生保健
  • 电信
  • 媒体与娱乐
  • 其他的

第 9 章：市场估计与预测：按地区，2021 - 2032

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 俄罗斯
    • 北欧人
    • 欧洲其他地区
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳新银行
    • 东南亚
    • 亚太地区其他地区
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
    • 拉丁美洲其他地区
  • MEA
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯
    • MEA 的其余部分

第 10 章：公司简介

  • Amazon
  • Baidu
  • Clearview AI
  • DuckDuckGoose AI
  • DuckDuckGoose AI
  • Facia
  • Ghiro AI
  • Google
  • Gradiant
  • iDenfy
  • Image Forgery Detector
  • Imagga
  • Intel
  • iProov
  • Meta AI
  • Microsoft Corporation
  • Primeau Forensics
  • Q-integrity
  • Sentinel AI
  • Truepic
简介目录
Product Code: 9056

Global Fake Image Detection Market size will record a 20% CAGR between 2024 and 2032, driven by technological innovations in AI and machine learning. As instances of manipulated visuals rise, driven by digital media's pervasive influence, the demand for advanced detection tools intensifies. These innovations empower businesses, governments, and online platforms to safeguard integrity, combat deception, and preserve public trust in digital content. This trend underscores a crucial shift towards proactive measures for identifying and mitigating the impact of fake images across various sectors and societal contexts.

For instance, in May 2024, OpenAI launched a tool to detect AI-generated images, marking and protecting digital content to combat misinformation, especially during critical events like elections. This development suggests an increasing demand for technologies that can effectively identify and safeguard against AI-generated images, particularly during sensitive periods such as elections. It highlights a potential expansion in the market as organizations seek advanced tools to combat the proliferation of manipulated visuals and maintain trust in digital content integrity.

The fake image detection industry is segmented based on offering, deployment model, organization size, end user, and region.

The large enterprises segment will establish a considerable foothold by 2032, leveraging substantial resources for advanced technologies and robust cybersecurity measures. These enterprises face heightened risks from malicious actors spreading misinformation. Investments in AI and machine learning solutions empower them to detect and mitigate fake images effectively. Moreover, compliance requirements and reputation management drive the adoption of sophisticated detection tools. As guardians of brand integrity and public trust, large enterprises are pivotal in shaping the evolving landscape of fake image detection technologies.

The BFSI segment will amass notable gains by 2032, attributed to the sector's high vulnerability to fraud and reputational risks. Financial institutions increasingly rely on advanced AI and machine learning algorithms to detect manipulated images used in fraudulent activities like identity theft and forged documents. Regulatory compliance mandates and customer trust preservation further drive adoption. As financial transactions move increasingly online, the BFSI segment plays a critical role in advancing the efficacy and adoption of fake image detection technologies.

Asia Pacific fake image detection market share will achieve a remarkable CAGR from 2024 to 2032, owing to rapid digitalization, increasing internet penetration, and rising instances of misinformation. Governments and enterprises across the region are investing in AI-driven technologies to combat fake images. Additionally, the presence of major technology firms and a burgeoning startup ecosystem contribute to Asia Pacific's role as a significant contributor to the global fake image detection industry.

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates and calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimates
  • 1.3 Forecast model
  • 1.4 Primary research & validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market definitions

Chapter 2 Executive Summary

  • 2.1 Industry 360 degree synopsis, 2021 - 2032

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 Data providers
    • 3.2.2 Technology developers
    • 3.2.3 Software vendors
    • 3.2.4 System integrators
    • 3.2.5 Cloud service providers
  • 3.3 Profit margin analysis
  • 3.4 Technology & innovation landscape
  • 3.5 Patent analysis
  • 3.6 Key news & initiatives
  • 3.7 Regulatory landscape
  • 3.8 Impact forces
    • 3.8.1 Growth drivers
      • 3.8.1.1 The proliferation of misinformation and disinformation
      • 3.8.1.2 Advancements in artificial intelligence (AI) and machine learning (ML)
      • 3.8.1.3 Protecting the brand reputation of businesses and organizations
      • 3.8.1.4 Government regulatory compliance to regulate the use of fake images
    • 3.8.2 Industry pitfalls & challenges
      • 3.8.2.1 Evolving techniques of image manipulation
      • 3.8.2.2 High volume and diversity of image data
  • 3.9 Growth potential analysis
  • 3.10 Porter's analysis
  • 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Offering, 2021 - 2032 ($Bn)

  • 5.1 Key trends
  • 5.2 Software
    • 5.2.1 Deepfake image detection
    • 5.2.2 Photoshopped image detection
    • 5.2.3 AI-generated image detection
    • 5.2.4 Real-time verification
    • 5.2.5 Others
  • 5.3 Services
    • 5.3.1 Consulting services
    • 5.3.2 Integration & deployment
    • 5.3.3 Support & maintenance

Chapter 6 Market Estimates & Forecast, By Deployment Model, 2021 - 2032 ($Bn)

  • 6.1 Key trends
  • 6.2 On-premises
  • 6.3 Cloud

Chapter 7 Market Estimates & Forecast, By Organization Size, 2021 - 2032 ($Bn)

  • 7.1 Key trends
  • 7.2 Large enterprises
  • 7.3 SMEs

Chapter 8 Market Estimates & Forecast, By End User, 2021 - 2032 ($Bn)

  • 8.1 Key trends
  • 8.2 BFSI
  • 8.3 Government
  • 8.4 Healthcare
  • 8.5 Telecom
  • 8.6 Media & entertainment
  • 8.7 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2032 ($Bn)

  • 9.1 Key trends
  • 9.2 North America
    • 9.2.1 U.S.
    • 9.2.2 Canada
  • 9.3 Europe
    • 9.3.1 UK
    • 9.3.2 Germany
    • 9.3.3 France
    • 9.3.4 Spain
    • 9.3.5 Italy
    • 9.3.6 Russia
    • 9.3.7 Nordics
    • 9.3.8 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 China
    • 9.4.2 India
    • 9.4.3 Japan
    • 9.4.4 South Korea
    • 9.4.5 ANZ
    • 9.4.6 Southeast Asia
    • 9.4.7 Rest of Asia Pacific
  • 9.5 Latin America
    • 9.5.1 Brazil
    • 9.5.2 Mexico
    • 9.5.3 Argentina
    • 9.5.4 Rest of Latin America
  • 9.6 MEA
    • 9.6.1 UAE
    • 9.6.2 South Africa
    • 9.6.3 Saudi Arabia
    • 9.6.4 Rest of MEA

Chapter 10 Company Profiles

  • 10.1 Amazon
  • 10.2 Baidu
  • 10.3 Clearview AI
  • 10.4 DuckDuckGoose AI
  • 10.5 DuckDuckGoose AI
  • 10.6 Facia
  • 10.7 Ghiro AI
  • 10.8 Google
  • 10.9 Gradiant
  • 10.10 iDenfy
  • 10.11 Image Forgery Detector
  • 10.12 Imagga
  • 10.13 Intel
  • 10.14 iProov
  • 10.15 Meta AI
  • 10.16 Microsoft Corporation
  • 10.17 Primeau Forensics
  • 10.18 Q-integrity
  • 10.19 Sentinel AI
  • 10.20 Truepic