Dataset Name: Banknote Authentication Dataset

Description:

This dataset contains a collection of features extracted from images of genuine and counterfeit banknotes. It's commonly used to train and evaluate machine learning models for automated banknote authentication, aiming to distinguish real banknotes from forgeries.

Features:

Each data point represents a single banknote and includes the following features: Variance of Wavelet Transformed image (continuous): Measures image texture variation. Skewness of Wavelet Transformed image (continuous): Quantifies image asymmetry. Curtosis of Wavelet Transformed image (continuous): Captures image tailedness. Entropy of image (continuous): Reflects image randomness or information content. Class (categorical): Indicates whether the banknote is genuine (1) or counterfeit (0). Number of Instances:

The dataset typically contains several hundred banknote images, with approximately equal proportions of genuine and counterfeit examples. Source:

The dataset was originally collected by researchers at the University of Applied Sciences, Ostwestfalen-Lippe, Germany. Applications:

Develop and evaluate machine learning models for banknote authentication. Compare the performance of different classification algorithms in this domain. Explore feature engineering techniques to improve model accuracy. Investigate the effectiveness of various feature selection methods for identifying the most informative features for authentication. Additional Notes:

The dataset is often used as a benchmark for classification tasks due to its balanced class distribution and relatively simple feature set. It's essential to consider data preprocessing techniques (e.g., normalization, handling missing values) before model training. Model evaluation should involve metrics suitable for imbalanced classes if the distribution of genuine and counterfeit notes is skewed.

A dataset examining trust, authenticity and deception concerns in an AI-shaped media environment, including uncertainty about what is real, confidence spotting synthetic signals, verification habits, and the emotional strain of not knowing what to trust.

This dataset is a carefully compiled collection of 15,000 text samples developed specifically for document forensics research and education. Each entry is labeled to indicate whether the document is authentic (0) or forged (1), reflecting real-world inspired cases involving sensitive legal, administrative, and investigative contexts. The content has been prepared using original sources and verified references, simulating scenarios where document integrity is critical—such as legal proceedings, compliance audits, and fraud investigations. Due to the highly sensitive and realistic nature of some of the content, this dataset is strictly intended for educational and non-commercial use only. It must not be used in production environments or for any purpose beyond academic study. By providing access to nuanced and challenging textual data, this dataset supports ethical research into document validation, helping learners and researchers explore the complexities of digital text authenticity in a safe, responsible manner.

Content Authenticity Platform Market Outlook

According to our latest research, the global Content Authenticity Platform market size reached USD 1.42 billion in 2024, reflecting the surging demand for advanced content verification tools amid rising concerns over misinformation and digital content manipulation. The market is experiencing robust expansion, with a projected CAGR of 22.6% between 2025 and 2033, and is expected to reach USD 9.12 billion by 2033. This growth is primarily driven by the proliferation of synthetic media, increasing regulatory scrutiny, and the urgent need for organizations to safeguard their digital assets and reputation in a rapidly evolving media landscape.

One of the primary growth factors fueling the Content Authenticity Platform market is the exponential rise in manipulated digital content, such as deepfakes and AI-generated imagery. As digital transformation accelerates across industries, the sheer volume of user-generated and professionally produced content has made it increasingly challenging to distinguish authentic content from fabricated or altered material. This has heightened the demand for sophisticated authentication solutions capable of verifying the integrity and provenance of digital assets in real-time. Organizations, particularly in sectors like media, entertainment, and finance, are investing heavily in content authenticity platforms to mitigate reputational risks, comply with emerging regulations, and maintain audience trust. Furthermore, as deep learning technologies continue to evolve, the sophistication of content manipulation techniques is also increasing, necessitating continuous innovation in verification tools and platforms.

Another significant driver is the growing regulatory focus on digital content authenticity and data privacy. Governments and regulatory bodies across the globe are introducing stringent guidelines and compliance requirements to curb the spread of misinformation, protect consumer data, and ensure the integrity of digital communications. For instance, the European Union’s Digital Services Act and similar legislation in North America and Asia Pacific are compelling organizations to adopt robust content verification mechanisms as part of their compliance strategies. This regulatory pressure is not only compelling large enterprises but also small and medium-sized businesses to integrate content authenticity platforms into their digital ecosystems. The rising prevalence of online fraud, identity theft, and brand impersonation is further amplifying the demand for these platforms, as organizations seek to protect both their internal operations and their customers from cyber threats.

The rapid adoption of cloud-based solutions and advancements in artificial intelligence are also playing a pivotal role in the expansion of the Content Authenticity Platform market. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, enabling organizations to seamlessly integrate content verification solutions into their existing workflows. Meanwhile, AI-powered platforms are becoming increasingly adept at detecting subtle signs of digital manipulation, identifying deepfakes, and verifying the authenticity of multimedia content across various formats. These technological advancements are making content authenticity solutions more accessible and effective, fostering widespread adoption across diverse industry verticals. In addition, the integration of blockchain technology for immutable content tracking and verification is emerging as a promising trend, further enhancing the reliability and transparency of content authenticity platforms.

From a regional perspective, North America currently dominates the Content Authenticity Platform market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading technology providers, high digital content consumption, and proactive regulatory measures are key factors contributing to the region’s leadership. Meanwhile, Asia Pacific is anticipated to witness the fastest growth rate during the forecast period, driven by rapid digitalization, increasing investment in cybersecurity infrastructure, and the proliferation of social media platforms. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing awareness of content authenticity challenges and increasing adoption of digital verification solutions across government and enterprise sectors.

With the rapid advancement of large language models (LLMs), detecting text generated by LLMs has garnered increasing attention. To address the scarcity of text detection datasets, this study proposes and constructs a high-quality, multi-domain text detection dataset, MDD-TD (Multi-Domain Text Detection Dataset), based on two detection tasks: source detection and content authenticity verification. The dataset sources encompass three dimensions: translation-optimized open-source data, web-crawled open-source data, and prompt-augmented synthetic data. Translation corpora were derived from the SimpleAI/HC3 dataset by selecting high-quality responses for translation and refinement. Web-crawled open-source corpora were obtained by scraping and curating data from Weibo and Douban platforms. Synthetic data was generated using rule-driven methods, leveraging existing translation and web-sourced data through various prompt strategies. For quality control, the PPL method was first applied to calculate perplexity distributions using language models, removing texts with abnormal perplexity. Semantic similarity-based deduplication maintained diversity, supplemented by manual review to ensure data authenticity and reliability. Ultimately, 30,000 high-quality data points were selected and stored in JSON format, containing text, source, and authenticity labels, and categorized into three core detection tasks: Q&A, comments, and news texts. The MDD-TD dataset holds significant value for text source tracing and content authenticity verification, supporting research and applications in large-model tasks such as generative detection and misinformation governance, thereby enhancing model credibility and security.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

Feature	Data Type	Description	Range or Example
IP Address	String	IP address belonging to the login attempt	0.0.0.0 - 255.255.255.255
Country	String	Country derived from the IP address	US
Region	String	Region derived from the IP address	New York
City	String	City derived from the IP address	Rochester
ASN	Integer	Autonomous system number derived from the IP address	0 - 600000
User Agent String	String	User agent string submitted by the client	Mozilla/5.0 (Windows NT 10.0; Win64; ...
OS Name and Version	String	Operating system name and version derived from the user agent string	Windows 10
Browser Name and Version	String	Browser name and version derived from the user agent string	Chrome 70.0.3538
Device Type	String	Device type derived from the user agent string	(mobile, desktop, tablet, bot, unknown)1
User ID	Integer	Idenfication number related to the affected user account	[Random pseudonym]
Login Timestamp	Integer	Timestamp related to the login attempt	[64 Bit timestamp]
Round-Trip Time (RTT) [ms]	Integer	Server-side measured latency between client and server	1 - 8600000
Login Successful	Boolean	True: Login was successful, False: Login failed	(true, false)
Is Attack IP	Boolean	IP address was found in known attacker data set	(true, false)
Is Account Takeover	Boolean	Login attempt was identified as account takeover by incident response team of the online service	(true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

• The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

• The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

• The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

• ASNs with values >= 500.000

• IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022)
Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono.
ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022,
 author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi},
 title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}},
 journal = {{ACM} {Transactions} on {Privacy} and {Security}},
 doi = {10.1145/3546069},
 publisher = {ACM},
 year  = {2022}
}

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

1. Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

In recent electoral contests, political observers and media outlets increasingly report on the level of “authenticity” of political candidates. However, even though this term has become commonplace in political commentary, it has received little attention in empirical electoral research. In this study, we identify the characteristics that we argue make a politician “authentic”. After theoretically discussing the different dimensions of this trait, we propose a survey battery aimed at measuring perceptions of the authenticity of political candidates. Testing our measure using data sets from different countries, we show that the answers to our items load on one latent concept that we call “authenticity”. Furthermore, perceptions of candidate authenticity seem to correlate strongly with evaluations of political parties and leaders, and with vote intention, while they are empirically distinguishable from other traits. We conclude that candidate authenticity is an important trait that should be taken into account by future research.

This page summarises how people experience authenticity, uncertainty and verification challenges in the age of AI-generated media, including trust in synthetic content, perceived deception risk, confidence identifying manipulated material, and the emotional impact of not knowing what is real.

AI Content Authenticity Verification Market Outlook

According to our latest research, the AI Content Authenticity Verification market size reached USD 1.24 billion in 2024, reflecting robust adoption across key sectors globally. The market is projected to grow at a CAGR of 26.7% from 2025 to 2033, reaching a forecasted value of USD 11.56 billion by 2033. This exponential growth is primarily driven by the increasing prevalence of deepfakes, misinformation, and synthetic media, which have prompted organizations and governments to invest heavily in advanced verification solutions to safeguard digital content integrity and trust.

The primary growth factor fueling the expansion of the AI Content Authenticity Verification market is the dramatic surge in manipulated and AI-generated content across digital platforms. As generative AI technologies become more sophisticated, so too does the potential for malicious actors to create realistic fake images, videos, and text. This has raised significant concerns among enterprises, news agencies, and regulatory bodies regarding the credibility of information disseminated online. Consequently, there is a pressing demand for robust AI-powered verification tools that can accurately detect, flag, and authenticate the originality of digital content in real-time. This demand is further amplified by the increasing reliance on digital communication and media, making authenticity verification a critical component of modern information ecosystems.

Another significant driver for the market is the evolving regulatory landscape, with governments and international organizations implementing stricter guidelines to combat misinformation and protect consumers from fraudulent content. For instance, the European Union’s proposed regulations on AI and digital content transparency have set a benchmark for other regions, compelling businesses to adopt advanced verification solutions to ensure compliance. Additionally, the proliferation of AI-generated content in sectors such as finance, healthcare, and e-commerce has heightened the need for specialized solutions that can address industry-specific risks. This regulatory push, combined with growing public awareness about the dangers of synthetic media, is accelerating the adoption of AI content authenticity verification technologies on a global scale.

Technological advancements in machine learning, natural language processing, and computer vision are also playing a pivotal role in shaping the AI Content Authenticity Verification market. The integration of blockchain with AI verification platforms is emerging as a transformative trend, providing immutable records of content provenance and further enhancing trust in digital assets. Moreover, the increasing availability of cloud-based verification solutions is making it easier for organizations of all sizes to deploy and scale these technologies without significant upfront investments. As a result, both large enterprises and small and medium-sized businesses are increasingly adopting AI-powered authenticity verification tools to protect their brands, customers, and data from the growing threat of digital misinformation.

From a regional perspective, North America currently dominates the AI Content Authenticity Verification market due to its advanced technological infrastructure, high digital content consumption, and proactive regulatory measures. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid digital transformation, expanding internet penetration, and increasing investments in cybersecurity. Europe remains a key market, buoyed by stringent data protection regulations and a strong focus on media integrity. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by rising awareness and government initiatives aimed at combating digital fraud and misinformation.

Component Analysis

The Component segment of the AI Content Authenticity Verifica

Single sign-on (SSO) is a session and user authentication service that permits a user to use one set of login credentials -- for example, a username and password -- to access multiple applications. SSO can be used by enterprises, small and midsize organizations, and individuals to ease the management of multiple credentials.

This dataset captures realistic simulations of news articles and social media posts circulating between 2024–2025, labeled for potential AI-generated misinformation.

It includes 500 rows × 31 columns, combining:
- Temporal features → date, time, month, day of week
- Text-based metadata → platform, region, language, topic
- Quantitative engagement metrics → likes, shares, comments, CTR, views
- Content quality indicators → sentiment polarity, toxicity score, readability index
- Fact-checking signals → credibility source score, manual check flag, claim verification status
- Target variable → is_misinformation (0 = authentic, 1 = misinformation)

This dataset is designed for machine learning, deep learning, NLP, data visualization, and predictive analysis research.

🎯 Use Cases

This dataset can be applied to multiple domains:
- 🧠 Machine Learning / Deep Learning: Binary classification of misinformation
- 📊 Data Visualization: Engagement trends, regional misinformation heatmaps
- 🔍 NLP Research: Fake news detection, text classification, sentiment-based filtering
- 🌐 PhD & Academic Research: AI misinformation studies, disinformation propagation models
- 📈 Model Evaluation: Feature engineering, ROC-AUC, precision-recall tradeoff

Content Authenticity Solutions Market Outlook

According to our latest research, the global Content Authenticity Solutions market size reached USD 2.1 billion in 2024, registering a robust growth trajectory. The market is projected to expand at a CAGR of 14.8% from 2025 to 2033, culminating in a forecasted value of USD 6.2 billion by 2033. This dynamic growth is primarily driven by the escalating proliferation of synthetic media, deepfakes, and digital misinformation, which have significantly heightened the demand for advanced content verification and authentication solutions across diverse industries.

One of the primary growth factors fueling the Content Authenticity Solutions market is the rapid advancement and democratization of generative artificial intelligence (AI) technologies. As AI-powered content creation tools become more accessible, the volume of synthetic images, videos, and audio circulating on digital platforms has surged. This trend has amplified concerns among enterprises, governments, and consumers regarding the authenticity of digital content. In response, organizations are increasingly investing in robust content authenticity solutions to safeguard brand reputation, ensure regulatory compliance, and maintain public trust. The rise of deepfakes in political campaigns, social media, and news dissemination has further accelerated the adoption of these solutions, as stakeholders seek to mitigate the risks associated with manipulated or misleading content.

Another major driver of market growth is the evolving regulatory landscape, which is compelling organizations to implement stringent content verification protocols. Governments and regulatory bodies worldwide are introducing new guidelines and frameworks to address the challenges posed by digital misinformation and content forgery. For instance, the European Union’s Digital Services Act and similar regulations in North America and Asia Pacific are setting higher standards for digital content integrity and transparency. This regulatory momentum is prompting enterprises, publishers, and digital platforms to adopt comprehensive content authenticity solutions that can verify the provenance, integrity, and originality of digital assets in real-time. The integration of blockchain technology and advanced cryptographic methods into content authentication workflows is further enhancing the reliability and scalability of these solutions.

The increasing digitalization of business operations and the surge in remote work environments have also contributed to the expansion of the Content Authenticity Solutions market. As organizations accelerate their digital transformation initiatives, the volume of digital content exchanged across internal and external channels has grown exponentially. This has heightened the risk of content tampering, unauthorized access, and data breaches. Enterprises are therefore prioritizing investments in content authenticity solutions that offer seamless integration with existing IT infrastructure, multi-layered security features, and real-time monitoring capabilities. The growing adoption of cloud-based deployment models and managed services is further democratizing access to advanced content authentication technologies, enabling small and medium enterprises (SMEs) to benefit from enterprise-grade security at an affordable cost.

From a regional perspective, North America continues to dominate the Content Authenticity Solutions market, accounting for the largest share in 2024. The region’s leadership can be attributed to the presence of major technology providers, a highly digitized economy, and proactive regulatory initiatives. Europe follows closely, driven by stringent data protection regulations and a strong emphasis on digital trust. Meanwhile, the Asia Pacific region is witnessing the fastest growth rate, fueled by rapid digital adoption, increasing incidences of digital fraud, and rising awareness about content authenticity among enterprises and government agencies. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing investments in digital infrastructure and cybersecurity.

Component Analysis

The Component segment of the Content Authenticity Solutions market is broadly categorized into software, hardware, and services. Software solutions form the backbone of this market, offering advanced algorithms for content verification, digital watermarking, provenance tracking, and real-time monitoring. These platforms le

The authentication method most used by companies worldwide was username and password, mentioned by nearly ** percent of respondents. At the same time, **** of respondents stated that their company was using software tokens such as one-time passwords. Overall, over ********** of respondents indicated using a biometric authentication method.

Digital Evidence Authenticity Certificates Market Outlook

According to our latest research, the global Digital Evidence Authenticity Certificates market size reached USD 1.43 billion in 2024, reflecting a robust surge in demand across key verticals. The market is projected to expand at a CAGR of 16.2% during the forecast period, reaching a value of USD 4.04 billion by 2033. This impressive growth is driven by the increasing necessity for verifiable, tamper-proof digital evidence in legal, law enforcement, and enterprise environments worldwide, as digital transformation accelerates and cyber threats become more sophisticated.

The rapid proliferation of digital devices and the exponential growth of digital data have been primary catalysts for the expansion of the Digital Evidence Authenticity Certificates market. As organizations and government bodies increasingly rely on electronic records, emails, surveillance footage, and other digital evidence in legal proceedings, the need for reliable mechanisms to authenticate the origin, integrity, and chain of custody of such evidence has intensified. This is particularly crucial given the rise in cybercrime, data breaches, and advanced forgery techniques that threaten the credibility of digital evidence. The implementation of authenticity certificates ensures that digital evidence is admissible in court and can withstand rigorous scrutiny, thereby fostering trust in digital legal processes and investigations.

Another significant growth factor for the Digital Evidence Authenticity Certificates market is the evolving regulatory landscape and the enforcement of stringent data protection and privacy laws across various jurisdictions. Regulatory frameworks such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar initiatives in Asia Pacific and Latin America are compelling organizations to adopt more robust digital evidence management practices. These regulations mandate the secure handling, storage, and verification of digital evidence, driving demand for advanced certificate-based solutions that can provide auditable proof of authenticity and compliance. The growing emphasis on digital forensics in both public and private sectors further underscores the critical role of authenticity certificates in maintaining evidentiary integrity.

Technological advancements are also fueling the market’s momentum. The integration of blockchain, artificial intelligence (AI), and machine learning (ML) technologies into digital evidence management platforms is revolutionizing how authenticity certificates are generated, validated, and maintained. Blockchain, for instance, offers an immutable ledger for recording evidence transactions, while AI and ML enhance the automation and accuracy of evidence verification processes. These innovations not only improve the efficiency of legal and investigative workflows but also mitigate the risks associated with manual errors and intentional tampering. As a result, enterprises and government agencies are increasingly investing in modern digital evidence authentication solutions to stay ahead in the evolving threat landscape.

From a regional perspective, North America currently dominates the Digital Evidence Authenticity Certificates market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has witnessed significant adoption across federal and state law enforcement agencies, driven by the high incidence of cybercrimes and the presence of a mature legal infrastructure. Europe is also experiencing substantial growth, buoyed by strong regulatory mandates and advancements in digital forensics. Meanwhile, the Asia Pacific region is emerging as a lucrative market, propelled by rapid digitalization, expanding IT infrastructure, and increasing investments in cybersecurity across countries such as China, India, and Japan.

Component Analysis

The Digital Evidence Authenticity Certi

This dataset is part of the Human Clarity Institute’s AI–Human Experience Data Series, examining how adults perceive authenticity in the age of AI-generated media. It measures how people judge whether online content is real, their confidence in detecting synthetic media, and the emotional and behavioural effects of uncertainty created by AI-mediated environments.The dataset includes:• validated 1–7 Likert-style items• measures of online authenticity judgement• confidence indicators for detecting AI-generated media• emotional and behavioural responses to uncertainty• strategies and cues used to verify online information• trust, caution, and perceived realism of AI media• open-text reflections on moments of uncertainty• demographic variables across six English-speaking countriesData were collected via Prolific in 2025 from adults in the United Kingdom, United States, Australia, New Zealand, Ireland, and Canada. All responses were fully cleaned, anonymised, and verified according to the Human Clarity Institute’s open-data publication protocol.This dataset contributes to understanding how AI-generated media shapes digital trust, authenticity perception, and human confidence in verification. It provides foundational data for tracking how synthetic media influences behaviour, certainty, and trust in the evolving AI era.

AI Image Authenticity Verification Market Outlook

According to our latest research, the AI Image Authenticity Verification market size reached USD 1.25 billion globally in 2024, reflecting a strong demand for advanced digital content verification solutions. The market is projected to grow at a robust CAGR of 18.7% from 2025 to 2033, with the value expected to reach USD 6.12 billion by 2033. This surge is primarily driven by the proliferation of AI-generated content and the urgent need for technologies that can reliably distinguish authentic images from manipulated or synthetic ones, ensuring trust and integrity across digital ecosystems.

The exponential growth of the AI Image Authenticity Verification market is fueled by the widespread adoption of generative AI tools, which have made image manipulation more accessible and sophisticated than ever before. As deepfakes and synthetic media become increasingly prevalent, industries such as media, e-commerce, and law enforcement are prioritizing investments in authenticity verification solutions to combat misinformation, fraud, and digital forgery. The integration of AI-based verification systems is now seen as a critical safeguard for maintaining brand reputation, regulatory compliance, and consumer trust in an era where visual content can be easily altered and weaponized.

Another significant growth factor is the rapid digital transformation across sectors, which has amplified the volume and velocity of image data circulating online. Enterprises and governments alike are grappling with the challenge of verifying the integrity of images in real time, especially in high-stakes environments like healthcare diagnostics, banking transactions, and legal investigations. The deployment of advanced technologies such as deep learning, computer vision, and blockchain within image authenticity verification tools is enabling organizations to automate the detection of tampered or synthetic images with greater accuracy and efficiency, thus driving further adoption and market expansion.

Moreover, regulatory and compliance pressures are intensifying the need for robust image verification mechanisms. Governments and industry bodies are enacting stricter data privacy and anti-fraud regulations, compelling organizations to implement systems that can verify the source and authenticity of digital images. This regulatory landscape is particularly pronounced in regions such as North America and Europe, where penalties for non-compliance are substantial. As a result, vendors in the AI Image Authenticity Verification market are innovating rapidly to deliver solutions that not only meet technical requirements but also align with evolving legal standards, further accelerating the market’s upward trajectory.

Regionally, North America currently dominates the market, accounting for more than 37% of the global revenue in 2024. This leadership is attributed to the presence of leading technology providers, high digital adoption rates, and a proactive approach to combating digital misinformation. However, Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 20.4% through 2033, driven by rapid urbanization, expanding internet penetration, and growing awareness of digital security threats. Europe also holds a significant share, underpinned by stringent regulatory frameworks and a strong emphasis on digital trust and transparency. Other regions, including Latin America and the Middle East & Africa, are gradually increasing their investments in AI-driven verification technologies as digital ecosystems mature.

Component Analysis

The Component segment of the AI Image Authenticity Verification market is categorized into software, hardware, and services, each playing a pivotal role in the deployment and effectiveness of verification solutions. Software forms the backbone of th

​The Canadian Food Inspection Agency (CFIA) collected samples of fruit juice to verify accurate representation of their composition. The juice samples were tested for authenticity by assessing the soluble solids, mineral, organic acids, preservative and sugar content of the products. Fruit juice adulteration can be done by adding sugars or acids, diluting with water, or substituting with less expensive juices (for example apple). Additional Information + CFIA's Food Fraud Annual Report 2022 to 2023 report

The Canadian Food Inspection Agency (CFIA) collects samples of honey from across Canada to test for adulteration with foreign sugars. Testing is done using two techniques. Samples in the datasets below were tested by the CFIA for the addition of C4 sugars using Stable Isotope Ratio Analysis (SIRA), and Nuclear Magnetic Resonance (NMR). Analyses were conducted by a contracted laboratory to detect these and other added foreign sugars, including C3 types. Additional Information: + CFIA's Food Fraud Annual Report 2022 to 2023 report + CFIA's Food Fraud Annual Report 2021 to 2022 + CFIA's Food Fraud Annual Report (2020 to 2021) report + CFIA's Honey authenticity surveillance results (2019 to 2020) report + CFIA's Enhanced honey authenticity surveillance (2018 to 2019) report + CFIA's Compliance and enforcement activities

The Dual-Task Emotion–Authenticity Facial Expression Dataset (GFFD-2025) is a carefully curated collection of facial images created to support research in emotion recognition and authenticity detection. Unlike traditional emotion datasets, it focuses not only on identifying which emotion a person expresses but also on whether the expression is genuine or acted, contributing to studies in artificial intelligence, affective computing, and human–computer interaction.

A total of 2,224 raw facial images were initially collected from voluntary participants. After quality assessment and manual verification, a subset was refined and curated for further research. The dataset repository includes approximately 1,900 raw facial images and around 1,500 cropped and augmented images, representing the cleaned and extended version of the original collection.

The dataset covers seven primary emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise; each subdivided into two authenticity categories: Genuine and Fake (Acted). Images were captured under controlled indoor conditions to ensure consistent lighting, neutral backgrounds, and stable face positioning. Genuine expressions were elicited via emotional recall or audiovisual stimuli, while fake expressions were intentionally acted. All data collection sessions were supervised by a certified psychologist to ensure ethical compliance and emotional validity.

Images were reviewed and labeled following micro-expression research principles, considering subtle cues such as eye involvement, facial symmetry, muscle tension, and temporal dynamics to distinguish genuine from acted expressions. Curated images were standardized to 224×224 pixels for compatibility with common deep learning frameworks.

To enhance dataset diversity and model robustness, images underwent preprocessing and augmentation, including rotation (±30°), width and height shifts (0.2), shear (0.15), zoom (0.2), horizontal flipping, random brightness and contrast adjustments, and normalization to the [0,1] range.

This dataset offers a practical benchmark for research in emotion recognition, authenticity detection, human behavior analysis, multitask learning, and explainable AI, enabling development of models sensitive to subtle psychological authenticity cues.

Data collection and labeling were conducted at Daffodil International University, Dhaka, Bangladesh, under strict ethical guidelines with informed consent from all participants. Sessions were supervised to ensure participant comfort and authenticity.

Supervisor: Md. Mizanur Rahman Lecturer, Department of Computer Science and Engineering Daffodil International University, Dhaka, Bangladesh Email: mizanurrahman.cse@diu.edu.bd

Data Collectors: Sarah Tasnim Diya (Email: diya15-5423@diu.edu.bd) Most. Jannatul Ferdos (Email: ferdos15-5453@diu.edu.bd) Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

A complete list of live websites vulnerable to CWE-345, compiled through global website indexing conducted by WebTechSurvey.