The Creation and Detection of Deepfakes

Deepfake technology, a fusion of deep learning and artificial intelligence, has emerged as a potent tool capable of crafting hyper-realistic yet entirely fabricated multimedia content. This comprehensive review explores the evolution, applications, and underlying principles of deepfake technology, emphasizing its potential implications for privacy, security, and the spread of misinformation. Using advanced deep learning algorithms, particularly Generative Adversarial Networks (GANs), deepfake technology manipulates facial features with remarkable precision, raising concerns about its malicious applications. The review examines the exponential growth in online content sharing facilitated by social media platforms and affordable devices, highlighting the convenience and accessibility but also the risks associated with the widespread use of deepfake technology. The core of deepfake technology, GANs, engages in an iterative competition between a discriminator and a generator, resulting in increasingly convincing synthetic data. A thorough review of the literature reveals significant research efforts focused on deepfake detection, leveraging techniques such as error-level analysis, CNN architectures, and hybrid approaches. The paper discusses regulatory measures, public awareness campaigns, and the critical role of digital forensic evaluation in mitigating deepfake threats. Challenges and concerns, including misinformation, privacy invasion, national security risks, and erosion of trust, are outlined. Mitigation strategies encompass advanced detection algorithms, regulatory frameworks, public awareness, and digital forensic evaluation, emphasizing the collaborative efforts required across technology developers, policymakers, the public, and digital forensic experts to navigate this evolving landscape and safeguard trust, privacy, and security in the digital age.

The development of satirical and fake news on digital platforms has source of major concern about the spread of misinformation and its control on society. As part of the Arabic language, fake news detection (FND) shows particular problems because of language difficulties and the scarcity of labeled data. FND on Arabic corpus utilizing deep learning (DL) contains leveraging advanced neural network (NN) techniques and methods to automatically recognize and classify deceptive data in the Arabic language text. This procedure is vital in combating the spread of disinformation and misinformation, promoting media literacy, and make sure the credibility of data sources for the Arabic-speaking community. Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) are common selections for FND because of their capability for learning hierarchical features and model sequential data from the text. In this view, this study develops a Mountain Gazelle Optimizer with Deep Learning-Driven Fake News Classification on Arabic Corpus (MGODL-FNCAC) technique. The presented MGODL-FNCAC approach aims to increase the performance of the fake news classification on the Arabic corpus. Primarily, the MGODL-FNCAC technique involves different stages of pre-processing to make the input data compatible for classification. For fake news detection, the MGODL-FNCAC technique applies the deep belief network (DBN) model. At last, the MGO approach can be used for the better hyperparameter tuning of the DBN approach, which supports in enhancing the overall training process and detection rate. The simulation outcomes of the MGODL-FNCAC technique can be examined on Arabic corpus data. The extensive outcomes exhibit the importance of the MGODL-FNCAC system over other methodologies with maximum accuracy of 97.68% and 95.14% on Covid19Fakes and Satirical dataset, respectively.

Nowadays, because of its increased popularity, Android is target to a growing number of attacks and malicious applications, with the purpose of stealing private information and consuming credit by subscribing to premium services. Most of the current commercial antivirus solutions use static signatures for malware detection, which may fail to detect different variants of the same malware and zero‐day attacks. In this paper, we present a behavior‐based, dynamic analysis security solution, called Android Malware Detection System, for detecting both well‐known and zero‐day malware. The proposed solution uses a machine learning classifier in order to differentiate between the behaviors of legitimate and malicious applications. In addition, it uses the application statistics for determining its reputation. The final decision is based on a combination of the classifier's result and the application reputation. The solution includes a unique and extensive set of data collectors, which gather application‐specific data that describe the behavior of the monitored application. We evaluated our solution on a set of legitimate and malicious applications and obtained a high accuracy of 0.985. Our system is able to detect zero‐day malware samples that are not detected by current commercial solutions. Our solution outperforms other similar solutions running on mobile devices. Copyright © 2015 John Wiley & Sons, Ltd.

The rapid performance improvement and versatility of Android smartphone make people's lives more and more dependent on it. Not only making smartphone carry more personal privacy information, but also making it a target for hackers. Many security-prevent tools collect application information from smartphones for application analysis, but this information may cause privacy concerns. This paper proposes a system called ShadowDroid, which uses dynamic analysis technology for Android applications. ShadowDroid establishes a VPN on the smartphone to intercept all the network traffic to collect the data needed for analysis. At the same time, through the method of string matching to find out the private information from the data, and then de-identify it to make sure that the uploaded analysis data does not contain any personal identifiable information. ShadowDroid classifies malicious applications into three types: spyware, botware, and ransomware. Knowing the type of malicious application allows users to find the right response strategy. In addition, for malicious applications that mix multiple malicious behaviors, ShadowDroid calculates the similarity between the uploaded analytical data and the standard feature set of each type, thereby helping the user to determine which malicious behaviors the malicious application may contain. The experiment confirmed that the accuracy of the classification result of ShadowDroid without the privacy information leakage of smartphone users is 90%, only slightly lower than the accuracy of 92% of [1].

Social media contain with a large amount of data and this large amount of data is filled with a misinformation, which forward mislead to make unusual wrong decision by the public, creates mostly negative public emotions, and leads to serious threats belongs to public safety and social order. The spread of misinformation on social networks has belongs to the Social media algorithms to become a widespread among scholars. After the study, we see that raw misinformation spread on social media for the research product and compared it with the original information to better understanding of the characteristics of the spread misinformation on social networks. This study declares that a deep learning method to perform information content analysis and information emotion analysis on misinformation dataset with original information and to adopt an analytic process to analyze network with the difference between misinformation and original true information in terms of diffusion network characteristics. The research indicates that the spread of misinformation on social media is by feature content and different emotions with consequent different changes. The related overall research findings follows the existing research for make a certain valuable contribution to the governance of raw misinformation and the maintenance of network order responsible to original information.

Social networks are filled with a large amount of misinformation, which often misleads the public to make wrong decisions, stimulates negative public emotions, and poses serious threats to public safety and social order. The spread of misinformation in social networks has also become a widespread concern among scholars. In the study, we took the misinformation spread on social media as the research object and compared it with true information to better understand the characteristics of the spread of misinformation in social networks. This study adopts a deep learning method to perform content analysis and emotion analysis on misinformation dataset and true information dataset and adopts an analytic network process to analyze the differences between misinformation and true information in terms of network diffusion characteristics. The research findings reveal that the spread of misinformation on social media is influenced by content features and different emotions and consequently produces different changes. The related research findings enrich the existing research and make a certain contribution to the governance of misinformation and the maintenance of network order.

Deepfake technology, driven by advancements in deep learning and large language models, has found widespread applications across various fields. In the context of physical health management and application, deepfake presents new possibilities for media production, athlete representation, training enhancement, and historical event recreation. This review explores the multifaceted applications of deepfake in the health industry with the help of various generative technologies like large language models, analyzing its potential to transform broadcasting, virtual athlete branding, and tactical simulation. While the technology offers numerous benefits, it also poses significant risks, such as the spread of misinformation, privacy violations, unfair competition, and ethical dilemmas. This paper addresses these challenges and discusses the regulatory measures needed to ensure the ethical deployment of deepfake technology in physical health. Additionally, it highlights emerging detection techniques and suggests proactive strategies for health organizations to mitigate deepfake‐related threats. The review concludes with an outlook on future innovations, emphasizing the importance of balancing technological advancement with legal and ethical considerations to safeguard the integrity of the health industry.

Android is the most widespread mobile operating system in the world. Due to its popularity, malware has been increasing every year steadily, which causes lots of problems to users, such as using the device's resources and transmitting private information without user's awareness. As malware has increased, anti-malware solutions have as well. Current anti-malware solutions often have very serious limitations and malware is becoming more apt to take advantage of them. In this paper, we present DDefender, a user-friendly application that detects Android malicious applications on device. DDefender is a comprehensive solution that utilizes static and dynamic analysis techniques to extract features from the user's device, then applies deep learning algorithm to detect malicious applications. At first, we use dynamic analysis to extract system calls, system information, network traffics, and requested permissions of an inspected application. Then we use static analysis to extract significant features from the inspected application such as application's components. By utilizing neural network and a large feature set of 1007 features, we evaluated our system with 4208 applications (2104 benign applications and 2104 malicious applications) and we achieved up to 95% accuracy.

This chapter offers a critical review of research on misinformation and social networking sites (SNSs). Using keywords related to misinformation and SNSs, this review examines relevant scholarship published since 2004. Content of relevant articles is summarized in terms of examined contexts, involved disciplines (e.g., public health, communication), methodological approaches, use of theory, and solutions presently offered for addressing this important problem. Disinformation and fake news are also included in the scope of this review. Current trends in research on misinformation and SNSs will be discussed. Results of this review suggest that misinformation on SNSs represents an issue facing many fields without a clear or easy solution. Four recommendations are derived from the present review: (1) performance of additional research on platforms other than Facebook or Twitter; (2) clarification of conceptualizations of misinformation and increased consistency in usage of terms; (3) greater integration of theory for enhancing understanding of how misinformation spreads and how to best correct misinformation once it proliferates on SNSs; and (4) promotion of interdisciplinary collaborations among researches investigating misinformation and SNSs. Directions for future research are also provided.

Identifying trustworthy information in the presence of noisy data contributed by numerous unvetted sources from online social media (e.g., Twitter, Facebook, and Instagram) has been a crucial task in the era of big data. This task, referred to as truth discovery, targets at identifying the reliability of the sources and the truthfulness of claims they make without knowing either a priori. In this work, we identified three important challenges that have not been well addressed in the current truth discovery literature. The first one is “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. For example, on Twitter, rumors, scams, and influence bots are common examples of sources colluding, either intentionally or unintentionally, to spread misinformation and obscure the truth. The second challenge is “data sparsity” or the “long-tail phenomenon” where a majority of sources only contribute a small number of claims, providing insufficient evidence to determine those sources’ trustworthiness. For example, in the Twitter datasets that we collected during real-world events, more than 90 percent of sources only contributed to a single claim. Third, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. In this paper, we develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above three challenges. In particular, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach. We further develop a distributed framework to implement the proposed truth discovery scheme using Work Queue in an HTCondor system. The evaluation results on three real-world datasets show that the SRTD scheme significantly outperforms the state-of-the-art truth discovery methods in terms of both effectiveness and efficiency.

SDN is a new type of network architecture. The core technology of the SDN is to separate the control plane of the network device from the data plane so as to achieve flexible control of network traffic. Such structure and characteristics have put forward higher requirements on the security protection capability of the SDN controller. However, there are still less researches on malicious applications for the SDN network architecture. This article aims at this problem, based on the analysis of the existing malicious application detection methods and on deep learning technology proposed by a detection method for SDN malicious applications. Finally, under the TensorFlow deep learning simulation environment Keras, 30 SDN malicious samples were studied and tested. The experimental data show that the detection rate of this method for malicious applications can reach 89%, which proves the feasibility and scientificity of the program.

Nanotechnology encompasses a broad market of multiple raw materials at scale of one billionth of meter. This market is in ongoing growing and serves many fields of science and industry, where the construction sector is no exception. Particularly, nanosilica is a material that has been recently used in the construction sector on a mass scale, but so far there is no full knowledge regarding its advantages and limitations on cement-based composites notwithstanding its current research and application. This study reports on the current advances and trends of the nanosilica synthesis and application in cement based-composites, as well as its influence on the fresh empirical, rheological, mechanical, and durability properties of these composites. The findings reported here could be useful to support better understanding regarding the effect of silica nanoparticles on the fresh, mechanical and durability performances of cement-based pastes mortars and concretes. Based on these identified outputs, benefits and limitations of the silica nanoparticles are addressed, and recommendations about its implementation are provided to carry out further performance and case studies.

In the big data era, it's important to identify trustworthy information from an influx of noisy data contributed by unvetted sources from online social media (e.g., Twitter, Instagram, Flickr). This task is referred to as truth discovery which aims at identifying the reliability of the sources and the truthfulness of claims they make without knowing either of them a priori. There are two important challenges that have not been well addressed in current truth discovery solutions. The first one is “misinformation spread” where a majority of sources are contributing to false claims, making the identification of truthful claims difficult. The second challenge is “data sparsity” where sources contribute a small number of claims, providing insufficient evidence to accomplish the truth discovery task. In this paper, we developed a Robust Truth Discovery (RTD) scheme to address the above two challenges. In particular, the RTD scheme explicitly quantifies different degrees of attitude that a source may express on a claim and incorporates the historical contributions of a source using a principled approach. The evaluation results on two real world datasetsshow that the RTD scheme significantly outperforms the state-of-the-art truth discovery methods.

We are happy to present this special issue of The Open Biomedical Engineering (TOBEJ) focused on the converging field of smart sensors and virtual physiological human (VPH) initiatives, in the framework of biomedical processing for e-health.

Many Internet of Things (IoT) services are currently tracked and regulated via mobile devices, making them vulnerable to privacy attacks and exploitation by various malicious applications. Current solutions are unable to keep pace with the rapid growth of malware and are limited by low detection accuracy, long discovery time, complex implementation, and high computational costs associated with the processor speed, power, and memory. Therefore, an automated intelligence technique is necessary for detecting apps containing malware and effectively predicting cyberattacks in mobile marketplaces. In this study, a system for classifying mobile marketplaces applications using real-world datasets is proposed, which analyzes the source code to identify malicious apps. A rich feature set of application programming interface (API) calls is proposed to capture the regularities in apps containing malicious content. Two feature-selection methods—Chi-Square and ANOVA—were examined in conjunction with ten supervised machine-learning algorithms. The detection accuracy of each classifier was evaluated to identify the most reliable classifier for malware detection using various feature sets. Chi-Square was found to have a higher detection accuracy as compared to ANOVA. The proposed system achieved a detection accuracy of 98.1% with a classification time of 1.22 s. Furthermore, the proposed system required a reduced number of API calls (500 instead of 9000) to be incorporated as features.