Search Results (43)

Face recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significant challenges and risks. Distinguishing between real and fake faces, i.e., face anti-spoofing (FAS), is crucial to the security of face recognition systems. With the advent of large-scale academic datasets in recent years, FAS based on deep learning has achieved a remarkable level of performance and now dominates the field. This paper systematically reviews the latest advancements in FAS based on deep learning. First, it provides an overview of the background, basic concepts, and types of FAS attacks. Then, it categorizes existing FAS methods from the perspectives of RGB (red, green and blue) modality and other modalities, discussing the main concepts, the types of attacks that can be detected, their advantages and disadvantages, and so on. Next, it introduces popular datasets used in FAS research and highlights their characteristics. Finally, it summarizes the current research challenges and future directions for FAS, such as its limited generalization for unknown attacks, the insufficient multi-modal research, the spatiotemporal efficiency of algorithms, and unified detection for presentation attacks and deepfakes. We aim to provide a comprehensive reference in this field and to inspire progress within the FAS community, guiding researchers toward promising directions for future work. Full article

Effectively identifying factors related to user satisfaction is crucial for evaluating customer experience. This study proposes a two-phase analytical framework that combines natural language processing techniques with hierarchical decision-making methods. In Phase 1, an ERNIE-LSTM-based emotion model (ELEM) is used to detect fake reviews from 4016 smartphone evaluations collected from JD.com (accuracy: 84.77%, recall: 84.86%, F1 score: 84.81%). The filtered genuine reviews are then analyzed using Biterm Topic Modeling (BTM) to extract key satisfaction-related topics, which are weighted based on sentiment scores and organized into a multi-criteria evaluation matrix through the Analytic Hierarchy Process (AHP). These topics are further clustered into five major factors: user-centered design (70.8%), core performance (10.0%), imaging features (8.6%), promotional incentives (7.8%), and industrial design (2.8%). This framework is applied to a comparative analysis of two smartphone stores, revealing that Huawei Mate 60 Pro emphasizes performance, while Redmi Note 11 5G focuses on imaging capabilities. Further clustering of user reviews identifies six distinct user groups, all prioritizing user-centered design and core performance, but showing differences in other preferences. In Phase 2, a comparison of word frequencies between product reviews and community Q and A content highlights hidden user concerns often missed by traditional single-source sentiment analysis, such as screen calibration and pixel density. These findings provide insights into how product design influences satisfaction and offer practical guidance for improving product development and marketing strategies. Full article

Hyperspectral imaging (HSI) is an advanced imaging technique that captures detailed spectral information across multiple fields. This review explores its applications in counterfeit detection, remote sensing, agriculture, medical imaging, cancer detection, environmental monitoring, mining, mineralogy, and food processing, specifically highlighting significant achievements from the past five years, providing a timely update across several fields. It also presents a cross-disciplinary classification framework to systematically categorize applications in medical, agriculture, environment, and industry. In counterfeit detection, HSI identified fake currency with high accuracy in the 400–500 nm range and achieved a 99.03% F1-score for counterfeit alcohol detection. Remote sensing applications include hyperspectral satellites, which improve forest classification accuracy by 50%, and soil organic matter, with the prediction reaching R² = 0.6. In agriculture, the HSI-TransUNet model achieved 86.05% accuracy for crop classification, and disease detection reached 98.09% accuracy. Medical imaging benefits from HSI’s non-invasive diagnostics, distinguishing skin cancer with 87% sensitivity and 88% specificity. In cancer detection, colorectal cancer identification reached 86% sensitivity and 95% specificity. Environmental applications include PM2.5 pollution detection with 85.93% accuracy and marine plastic waste detection with 70–80% accuracy. In food processing, egg freshness prediction achieved R² = 91%, and pine nut classification reached 100% accuracy. Despite its advantages, HSI faces challenges like high costs and complex data processing. Advances in artificial intelligence and miniaturization are expected to improve accessibility and real-time applications. Future advancements are anticipated to concentrate on the integration of deep learning models for automated feature extraction and decision-making in hyperspectral imaging analysis. The development of lightweight, portable HSI devices will enable more on-site applications in agriculture, healthcare, and environmental monitoring. Moreover, real-time processing methods will enhance efficiency for field deployment. These improvements seek to enhance the accessibility, practicality, and efficacy of HSI in both industrial and clinical environments. Full article

Online reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated (genuine) as well as computer-generated (fake) reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this work, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem. Full article

The European Union Drugs Agency has emphasized the increasing difficulty in monitoring the drug market due to the emergence of new psychoactive substances, often marketed as legal highs. The proliferation of fake pharmacies, drugstores, and e-commerce platforms has made access to illicit substances alarmingly rapid and inexpensive. These substances are readily available without medical prescriptions, lacking proper risk assessments or monitoring of potential adverse effects, raising significant public health concerns. Today, the relentless pursuit of validation and success—often, at any cost—has led to an exponential rise in the use of cognitive and mood enhancers. Such substances are frequently consumed to manage demands related to work, diet, sexuality, sleep, achievement, and interpersonal relationships. Consequently, investigating these phenomena is critically important for institutions, as they represent a serious threat to individual development and health. Developing effective preventive and protective systems is essential. This review provides an overview of currently available smart drugs, discussing their desired and adverse neuropharmacological effects, psychological implications, and cognitive decline resulting from their excessive and unregulated use. This review concludes that a multidisciplinary approach combining molecular identification, micro-morphological analysis, and chemical characterization is crucial for the accurate detection, monitoring, and risk mitigation of new psychoactive substances. Full article

In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Research on misinformation spans multiple disciplines, but technical surveys rarely focus on the medical domain. Existing medical misinformation surveys provide broad insights for various stakeholders but lack a deep dive into computational methods. This survey fills that gap by examining how fact-checking and fake news detection techniques are adapted to the medical field from a computer engineering perspective. Specifically, we first present manual and automatic approaches for fact-checking, along with publicly available fact-checking tools. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation. While this survey primarily serves researchers and technology experts, it can also provide valuable insights for policymakers working to combat health misinformation. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation. Full article

Background/Objectives: Fake pain expressions are more intense, prolonged, and include non-pain-related actions compared to genuine ones. Despite these differences, individuals struggle to detect deception in direct tasks (i.e., when asked to detect liars). Regarding neural correlates, while pain observation has been extensively studied, little is known about the neural distinctions between processing genuine, fake, and suppressed pain facial expressions. This study seeks to address this gap using authentic pain stimuli and an implicit emotional processing task. Methods: Twenty-four healthy women underwent an fMRI study, during which they were instructed to complete an implicit gender discrimination task. Stimuli were video clips showing genuine, fake, suppressed pain, and neutral facial expressions. After the scanning session, participants reviewed the stimuli and rated them indirectly according to the intensity of the facial expression (IE) and the intensity of the pain (IP). Results: Mean scores of IE and IP were significantly different for each category. A greater BOLD response for the observation of genuine pain compared to fake pain was observed in the pregenual anterior cingulate cortex (pACC). A parametric analysis showed a correlation between brain activity in the mid-cingulate cortex (aMCC) and the IP ratings. Conclusions: Higher IP ratings for genuine pain expressions and higher IE ratings for fake ones suggest that participants were indirectly able to recognize authenticity in facial expressions. At the neural level, pACC and aMCC appear to be involved in unveiling the genuine vs. fake pain and in coding the intensity of the perceived pain, respectively. Full article

Generative adversarial networks (GANs) have revolutionised various fields by creating highly realistic images, videos, and audio, thus enhancing applications such as video game development and data augmentation. However, this technology has also given rise to deepfakes, which pose serious challenges due to their potential to create deceptive content. Thousands of media reports have informed us of such occurrences, highlighting the urgent need for reliable detection methods. This study addresses the issue by developing a deep learning (DL) model capable of distinguishing between real and fake face images generated by StyleGAN. Using a subset of the 140K real and fake face dataset, we explored five different models: a custom CNN, ResNet50, DenseNet121, MobileNet, and InceptionV3. We leveraged the pre-trained models to utilise their robust feature extraction and computational efficiency, which are essential for distinguishing between real and fake features. Through extensive experimentation with various dataset sizes, preprocessing techniques, and split ratios, we identified the optimal ones. The 20k_gan_8_1_1 dataset produced the best results, with MobileNet achieving a test accuracy of 98.5%, followed by InceptionV3 at 98.0%, DenseNet121 at 97.3%, ResNet50 at 96.1%, and the custom CNN at 86.2%. All of these models were trained on only 16,000 images and validated and tested on 2000 images each. The custom CNN model was built with a simpler architecture of two convolutional layers and, hence, lagged in accuracy due to its limited feature extraction capabilities compared with deeper networks. This research work also included the development of a user-friendly web interface that allows deepfake detection by uploading images. The web interface backend was developed using Flask, enabling real-time deepfake detection, allowing users to upload images for analysis and demonstrating a practical use for platforms in need of quick, user-friendly verification. This application demonstrates significant potential for practical applications, such as on social media platforms, where the model can help prevent the spread of fake content by flagging suspicious images for review. This study makes important contributions by comparing different deep learning models, including a custom CNN, to understand the balance between model complexity and accuracy in deepfake detection. It also identifies the best dataset setup that improves detection while keeping computational costs low. Additionally, it introduces a user-friendly web tool that allows real-time deepfake detection, making the research useful for social media moderation, security, and content verification. Nevertheless, identifying specific features of GAN-generated deepfakes remains challenging due to their high realism. Future works will aim to expand the dataset by using all 140,000 images, refine the custom CNN model to increase its accuracy, and incorporate more advanced techniques, such as Vision Transformers and diffusion models. The outcomes of this study contribute to the ongoing efforts to counteract the negative impacts of GAN-generated images. Full article

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation. Full article

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants. Full article

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score. Full article

Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images. Full article

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information. Full article

Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions. Full article

The proliferation of misinformation presents a significant challenge in today’s information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the intent to cause harm. In some instances, individuals unwittingly share misinformation, driven by a desire to assist others without thorough research. However, there are also situations where misinformation involves negligence, or even intentional manipulation, with the aim of shaping the opinions and decisions of the target audience. Another key factor contributing to misinformation is its alignment with individual beliefs and emotions. This alignment magnifies the impact and influence of misinformation, as people tend to seek information that reinforces their existing beliefs. As a starting point, some 56 papers containing ‘misinformation detection’ in the title, abstract, or keywords, marked as “articles”, written in English, published between 2016 and 2022, were extracted from the Web of Science platform and further analyzed using Biblioshiny. This bibliometric study aims to offer a comprehensive perspective on the field of misinformation detection by examining its evolution and identifying emerging trends, influential authors, collaborative networks, highly cited articles, key terms, institutional affiliations, themes, and other relevant factors. Additionally, the study reviews the most cited papers and provides an overview of all selected papers in the dataset, shedding light on methods employed to counter misinformation and the primary research areas where misinformation detection has been explored, including sources such as online social networks, communities, and news platforms. Recent events related to health issues stemming from the COVID-19 pandemic have heightened interest within the research community regarding misinformation detection, a statistic which is also supported by the fact that half of the papers included in top 10 papers based on number of citations have addressed this subject. The insights derived from this analysis contribute valuable knowledge to address the issue, enhancing our understanding of the field’s dynamics and aiding in the development of effective strategies to detect and mitigate the impact of misinformation. The results spotlight that IEEE Access occupies the first position in the current analysis based on the number of published papers, the King Saud University is listed as the top contributor for the misinformation detection, while in terms of countries, the top-5 list based on the highest contribution to this area is made by the USA, India, China, Spain, and the UK. Moreover, the study supports the promotion of verified and reliable sources of data, fostering a more informed and trustworthy information environment. Full article