Search Results (40)

Image forgery detection, as an essential technique for analyzing image credibility, has experienced significant advancements recently. However, the forgery detection performance remains unsatisfactory in terms of meeting practical requirements. This is partly attributed to the limited availability of pixel-level annotated forgery samples and insufficient utilization of forgery traces. We try to mitigate these issues through three aspects: training data, network design, and training strategy. In the aspect of training data, we introduce iterative self-supervision which helps generate a large collection of pixel-level labeled single or composite forgery samples through one or more rounds of random copy-move, splicing, and inpainting, addressing the insufficient availability of forgery samples. In the aspect of network design, recognizing that characteristic anomalies are generally apparent at the boundary between true and fake regions, often aligning with image edges, we propose a new edge-guided learning module to effectively capture forgery traces at image edges. In the aspect of training strategy, we introduce progressive self-adversarial training, dynamically generating adversarial samples by gradually increasing the frequency and intensity of adversarial actions during training. This increases the detection difficulty, driving the detector to identify forgery traces from harder samples while maintaining a low computational cost. Comprehensive experiments have shown that the proposed method surpasses the leading competing methods, improving image-level forgery identification by 6.6% (from 73.8% to 80.4% on average F1 score) and pixel-level forgery localization by 15.2% (from 59.1% to 74.3% in average F1 score). 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

Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. However, existing graph-based methods often focus on static graph structures, which struggle to adapt to the evolving nature of these graphs. In this paper, we propose Adaptive-DecayRank, a real-time and adaptive anomaly detection model for dynamic graph streams. Our method extends the dynamic PageRank algorithm by incorporating an adaptive Bayesian updating mechanism, allowing nodes to dynamically adjust their decay factors based on observed graph changes. This enables real-time detection of sudden structural shifts, improving anomaly identification in streaming graphs. We evaluate Adaptive-DecayRank on multiple real-world security datasets, including DARPA and CTU-13, as well as synthetic dense graphs generated using RTM. Our experiments demonstrate that Adaptive-DecayRank outperforms state-of-the-art methods, such as AnomRank, Sedanspot, and DynAnom, achieving up to $13.94\%$ higher precision, $8.43\%$ higher AUC, and more robust detection in highly dynamic environments. 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

Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF-IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification. 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

With the development of internet technology, fake news has become a multi-modal collection. The current news detection methods cannot fully extract semantic information between modalities and ignore the rumor properties of fake news, making it difficult to achieve good results. To address the problem of the accurate identification of multi-modal fake news, we propose the Vae-Clip multi-modal fake news detection model. The model uses the Clip pre-trained model to jointly extract semantic features of image and text information using text information as the supervisory signal, solving the problem of semantic interaction across modalities. Moreover, considering the rumor attributes of fake news, we propose to fuse semantic features with rumor style features using multi-feature fusion to improve the generalization performance of the model. We use a variational autoencoder to extract rumor style features and combine semantic features and rumor features using an attention mechanism to detect fake news. Numerous experiments were conducted on four datasets primarily composed of Weibo and Twitter, and the results show that the proposed model can accurately identify fake news and is suitable for news detection in complex scenarios, with the highest accuracy reaching 96.3%. Full article

The phenomenon of disinformation raises serious questions for society, affecting public trust and democratic stability. In this context, an attempt is made to configure a profile of the practices of identification and fight against disinformation, assess the incidence of social networks, and identify citizens’ perceptions of media literacy in Ecuador. The methodology used is quantitative and qualitative, with a descriptive approach, using a survey, interviews with experts, and focus groups. The converging points between experts and citizens are the need to develop media literacy processes that begin in basic education and the institutionalisation of the fight against disinformation, which should be assumed through an articulation between citizens and schools. On the other hand, training to identify fake news is directly related to information verification practices. Likewise, statistical evidence shows that Ecuadorians who verify information perceive themselves as fully informed citizens. Full article

In today’s age of social networking, web news inconsistencies have become a pressing concern. These discrepancies can mislead individuals when making important purchase decisions. Despite the existing research in this area, there is a need for more empirical and rigorous investigation into the inconsistencies reported in reviews. False reporting and disinformation on social media platforms can significantly impact societal stability and peace. Fake news is frequently disseminated on social media and can easily influence and deceive populations and governments. Many researchers are working toward distinguishing fake news from genuine news on social media platforms. The practical and timely identification of fake news can help prevent its spread. Our study focuses on how machine learning and deep learning algorithms are used to detect fraudulent data. The most fundamental and practical techniques deployed over recent years are investigated, classified, and defined in numerous datasets in an extended review model. Additionally, simulation media and recorded indicators of performance are reviewed in detail. The review, as mentioned above, provides a comprehensive analysis of key research findings, delving into pertinent issues that may impact individuals in the academic and professional realms interested in augmenting the reliability of automated FND models. Full article

Misinformation, fake news, and various propaganda techniques are increasingly used in digital media. It becomes challenging to uncover propaganda as it works with the systematic goal of influencing other individuals for the determined ends. While significant research has been reported on propaganda identification and classification in resource-rich languages such as English, much less effort has been made in resource-deprived languages like Hindi. The spread of propaganda in the Hindi news media has induced our attempt to devise an approach for the propaganda categorization of Hindi news articles. The unavailability of the necessary language tools makes propaganda classification in Hindi more challenging. This study proposes the effective use of deep learning and transformer-based approaches for Hindi computational propaganda classification. To address the lack of pretrained word embeddings in Hindi, Hindi Word2vec embeddings were created using the H-Prop-News corpus for feature extraction. Subsequently, three deep learning models, i.e., CNN (convolutional neural network), LSTM (long short-term memory), Bi-LSTM (bidirectional long short-term memory); and four transformer-based models, i.e., multi-lingual BERT, Distil-BERT, Hindi-BERT, and Hindi-TPU-Electra, were experimented with. The experimental outcomes indicate that the multi-lingual BERT and Hindi-BERT models provide the best performance, with the highest F1 score of 84% on the test data. These results strongly support the efficacy of the proposed solution and indicate its appropriateness for propaganda classification. Full article

Social media has become a popular platform for accessing and sharing news, but it has also led to a rise in fake news, posing serious risks. The ease of dissemination and constant flow of information raise concerns about the spread of incorrect information. Timely verification of news is crucial to combat false news. However, most research on false news identification has focused on English, neglecting South Asian languages. This study examines a dataset of Sindhi tweets, employing text feature extraction techniques such as TF–IDF and hashing vectorizer. Several machine learning algorithms, along with advanced deep learning models such as Transformer BERT, were utilized for analysis. Full article

Electronic commerce (E-commerce) transactions require secure communication to protect sensitive information such as credit card numbers, personal identification, and financial data from unauthorized access and fraud. Encryption using public key cryptography is essential to ensure secure electronic commerce transactions. RSA and Rabin cryptosystem algorithms are widely used public key cryptography techniques, and their security is based on the assumption that it is computationally infeasible to factorize the product of two large prime numbers into its constituent primes. However, existing variants of RSA and Rabin cryptosystems suffer from issues like high computational complexity, low speed, and vulnerability to factorization attacks. To overcome the issue, this article proposes a new method that introduces the concept of fake-modulus during encryption. The proposed method aims to increase the security of the Rabin cryptosystem by introducing a fake-modulus during encryption, which is used to confuse attackers who attempt to factorize the public key. The fake-modulus is added to the original modulus during encryption, and the attacker is unable to distinguish between the two. As a result, the attacker is unable to factorize the public key and cannot access the sensitive information transmitted during electronic commerce transactions. The proposed method’s performance is evaluated using qualitative and quantitative measures. Qualitative measures such as visual analysis and histogram analysis are used to evaluate the proposed system’s quality. To quantify the performance of the proposed method, the entropy of a number of occurrences for the pixels of cipher text and differential analysis of plaintext and cipher text is used. When the proposed method’s complexity is compared to a recent variant of the Rabin cryptosystem, it can be seen that it is more complex to break the proposed method—represented as $\mathrm{O}\left(ɲ\times \mathsf{\tau }\right)$which is higher than Rabin-P ($\mathrm{O}(ɲ))$ algorithms. Full article

With the popularization of digital technology, the problem of information pollution caused by fake news has become more common. Malicious dissemination of harmful, offensive or illegal content may lead to misleading, misunderstanding and social unrest, affecting social stability and sustainable economic development. With the continuous iteration of artificial intelligence technology, researchers have carried out automatic and intelligent news data mining and analysis based on aspects of information characteristics and realized the effective identification of fake news information. However, the current research lacks the application of multidisciplinary knowledge and research on the interpretability of related methods. This paper focuses on the existing fake news detection technology. The survey includes fake news datasets, research methods for fake news detection, general technical models and multimodal related technical methods. The innovation contribution is to discuss the research progress of fake news detection in communication, linguistics, psychology and other disciplines. At the same time, it classifies and summarizes the explainable fake news detection methods and proposes an explainable human-machine-theory triangle communication system, aiming at establishing a people-centered, sustainable human–machine interaction information dissemination system. Finally, we discuss the promising future research topics of fake news detection technology. Full article

There are several possibilities to improve classification in natural language processing tasks. In this article, we focused on the issue of coreference resolution that was applied to a manually annotated dataset of true and fake news. This dataset was used for the classification task of fake news detection. The research aimed to determine whether performing coreference resolution on the input data before classification or classifying them without performing coreference resolution is more effective. We also wanted to verify whether it is possible to enhance classifier performance metrics by incorporating coreference resolution into the data preparation process. A methodology was proposed, in which we described the implementation methods in detail, starting from the identification of entity mentions in the text using the neuralcoref algorithm, then through word-embedding models (TF–IDF, Doc2Vec), and finally to several machine learning methods. The result was a comparison of the implemented classifiers based on the performance metrics described in the theoretical part. The best result for accuracy was observed for the dataset with coreference resolution applied, which had a median value of 0.8149, while for the F1 score, the best result had a median value of 0.8101. However, the more important finding is that the processed data with the application of coreference resolution led to an improvement in performance metrics in the classification tasks. Full article

With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%. Full article