Fake Ones Research Articles

Mastering Deepfake Detection: A Cutting-edge Approach to Distinguish GAN and Diffusion-model Images

Detecting and recognizing deepfakes is a pressing issue in the digital age. In this study, we first collected a dataset of pristine images and fake ones properly generated by nine different Generative Adversarial Network (GAN) architectures and four Diffusion Models (DM). The dataset contained a total of 83,000 images, with equal distribution between the real and deepfake data. Then, to address different deepfake detection and recognition tasks, we proposed a hierarchical multi-level approach. At the first level, we classified real images from AI-generated ones. At the second level, we distinguished between images generated by GANs and DMs. At the third level (composed of two additional sub-levels), we recognized the specific GAN and DM architectures used to generate the synthetic data. Experimental results demonstrated that our approach achieved more than 97% classification accuracy, outperforming existing state-of-the-art methods. The models obtained in the different levels turn out to be robust to various attacks such as JPEG compression (with different quality factor values) and resize (and others), demonstrating that the framework can be used and applied in real-world contexts (such as the analysis of multimedia data shared in the various social platforms) for support even in forensic investigations to counter the illicit use of these powerful and modern generative models. We are able to identify the specific GAN and DM architecture used to generate the image, which is critical in tracking down the source of the deepfake. Our hierarchical multi-level approach to deepfake detection and recognition shows promising results in identifying deepfakes allowing focus on underlying task by improving (about 2% on the average) standard multiclass flat detection systems. The proposed method has the potential to enhance the performance of deepfake detection systems, aid in the fight against the spread of fake images, and safeguard the authenticity of digital media.

A Deniable Encryption Method for Modulation-Based DNA Storage.

Recent advancements in synthesis and sequencing techniques have made deoxyribonucleic acid (DNA) a promising alternative for next-generation digital storage. As it approaches practical application, ensuring the security of DNA-stored information has become a critical problem. Deniable encryption allows the decryption of different information from the same ciphertext, ensuring that the "plausible" fake information can be provided when users are coerced to reveal the real information. In this paper, we propose a deniable encryption method that uniquely leverages DNA noise channels. Specifically, true and fake messages are encrypted by two similar modulation carriers and subsequently obfuscated by inherent errors. Experiment results demonstrate that our method not only can conceal true information among fake ones indistinguishably, but also allow both the coercive adversary and the legitimate receiver to decrypt the intended information accurately. Further security analysis validates the resistance of our method against various typical attacks. Compared with conventional DNA cryptography methods based on complex biological operations, our method offers superior practicality and reliability, positioning it as an ideal solution for data encryption in future large-scale DNA storage applications.

Fingerprint liveness detection based on contourlet, various entropy algorithms and multiobjective genetic algorithm-based ensemble classifier

ABSTRACT Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems.

Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems.

Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (≤70 °C) solution process for fabricating DUV synapses based on PEA2PbBr4/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.

Detecting Fraud App Using Sentimental Analysis By IJISRT

The software used in fake mobile applications mimics the functions of real, reliable, and legitimate applications. When these applications are released, they frequently display advertisements, affect personal information in the system, infect devices with viruses, etc. to make money. It causes malicious behaviour such as Most users cannot distinguish between real apps and fake ones. That is why people always review user reviews before installing an app. In this article, we introduce a website where users can learn more about an application before installing it. The results are based on previous reviews and ratings provided by users and provide an opportunity to determine the user experience for a particular mobile application. More importantly, we will evaluate the evaluation using emotional evaluation to check whether the text is emotional, that is, whether the text is emotional, i.e., whether the text is positive, negative, or negative.

Evaluating the Effectiveness of a Gan Fingerprint Removal Approach in Fooling Deepfake Face Detection

Deep neural networks are able to generate stunningly realistic images, making it easy to fool both technology and humans into distinguishing real images from fake ones. Generative Adversarial Networks (GANs) play a significant role in these successes (GANs). Various studies have shown that combining features from different domains can produce effective results. However, the challenges lie in detecting these fake images, especially when modifications or removal of GAN components are involved. In this research, we analyse the high-frequency Fourier modes of real and deep network-generated images and show that Images generated by deep networks share an observable, systematic shortcoming when it comes to reproducing their high-frequency features. We illustrate how eliminating the GAN fingerprint in modified pictures' frequency and spatial spectrum might affect deep-fake detection approaches. In-depth review of the latest research on the GAN-Based Artifacts Detection Method. We empirically assess our approach to the CNN detection model using style GAN structures 140k datasets of Real and Fake Faces. Our method has dramatically reduced the detection rate of fake images by 50%. In our study, we found that adversaries are able to remove the fingerprints of GANs, making it difficult to detect the generated images. This result confirms the lack of robustness of current algorithms and the need for further research in this area.

PERAN IMIGRASI KELAS 1 KOTA MALANG DALAM MENANGANI TENAGA KERJA INDONESIA ILEGAL (SELUNDUPAN NON PROSEDURAL) (Studi Pada Kantor Imigrasi Kelas 1 Kota Malang)

This research aims to find out and analyze the role of Class I Immigration in Malang City in terms of its services to help handle and prevent Indonesian workers or what are usually called TKI from being illegally smuggled non-procedurally. Around 3 thousand Malang City residents leave every year to become Indonesian Migrant Workers (PMI) to the Asia Pacific region. This figure is data officially written at the Malang City Manpower Service (Disnaker). Meanwhile, for non-procedural or illegal PMIs which are managed by Private Indonesian Migrant Worker Placement Implementers (PPTKIS) outside the region or fake ones. This research uses the Qualitative Descriptive Method and adopts analysis from the New Public Management Theory in the services provided and determines the role of Class I Immigration in Malang City. From this research, the results were obtained and that the services provided were good in accordance with the applicable SOP, but the obstacles that occurred lay in the individual members of the community who would become migrant workers. It is hoped that in the future the Class I Immigration of Malang City will be able to more discipline in examining documents that are prone to being fake and the need for cross-sector integration between several related agencies or departments in carrying out their duties. No less important is socializing the dangers of illegal migrant workers to the public in order to raise public awareness of the process of becoming Indonesian workers.

The Impact of Online Reviews Manipulation on Consumer Purchase Decision Based on The Perspective of Consumers’ Perception

<p>Consumers depend on online reviews to influences their purchase decisions. On account of that,many vendors and retailers try to manipulate online reviews to mislead potential consumers to take risky purchase decisions. Many scholars have conducted a lot of research on the impact of online product reviews on consumer behavior and sales. However, the existing work are mainly based on the premise of real product reviews, but few attentions have been paid of fake ones. Based on the recognition results of deceptive reviews, this article explores whether consumers be aware or perceive it when deceptive reviews are flooding the online review system, and further analyze what influence will be imposed on final purchase decision with different perception. The empirical analysis of the questionnaire survey show that in the context of two different perceptions of consumers, deceptive reviews have significant differences in the results of purchase decisions. In addition, research also shows that consumers’ persuasive knowledge plays a moderating role between perceived deception and purchase decision.</p> <p> </p>

Certificate Verification and Counterfeit Detection using Blockchain

Abstract: Certificates help students not only to prove their achieved goals and milestones but also ensure that he/she maintains a high level of knowledge in that particular field. An estimated total of 25.57 crore Indian students have been enrolled for primary to higher education in 2020–21 and nearly 65 lakhs of them graduate each year. Throughout this journey, a student generates a myriad number of certificates that may include results, transcripts, degrees, diplomas, etc. A student has to submit these certificates to apply for a job or seek higher admission in any particular organization. A major problem today is manually verifying and authenticating these certificates. Many hardworking people with genuine certificates get rejected and suffer because of the lack of a system that can differentiate original certificates from fake ones. With easy access to cheap and advanced software, document forgery has become a matter of concern nowadays. This scenario demands an updated system that could not only store documents safely but also help verify and authenticate them, their issuers, and holders in a way that is much simpler, effective, and secure. Blockchain technology comes up as a solution to all these problems. Blockchain has recently emerged as a potential means for the document-authentication process and can be easily used to tackle document forgery and counterfeiting as it follows a decentralized approach. Our proposed model includes several methods such as unique hash generation, key cryptography, digital ledgers, proof of work, digital signatures, and distributed storage which has made the document-verification process easier and more secure for both the certificate-generating organization and the holder of the certificate. The SHA-256 algorithm has been used to assign a unique hash to each uploaded document which can be used to validate its authenticity. Thus, this system meets up all the criteria for a document verification system by overcoming the drawbacks and difficulties currently faced in the traditional methods of document verification.

Evaluating Fake News Detection Models

Fake news is untrue information presented as news. It often the aim of damaging the reputation of a person or entity or making money through advertising like a picture, short film, song, etc., revenue. Due to exponential growth of information in online, it is very impossible to figure out the true from the false. Some of the social media networks such as Twitter, Instagram and Facebook are affected by their user through fake news, and it is very hard to catch the fake stories before they go viral. Today most of the people prefer to search and absorb media news instead conventional one. Now days the widespread distribution of fake news may affect people and society. So we decide to build a web app to detect the fake news. The credibility and trust in the news media are at an all-time low with an object that is made to look real or valuable in order to deceive people. It is becoming increasingly difficult to determine which news is real and which is fake various machine learning methods have been used to separate real news from fake ones like experts revealed that the painting was a fake. There are some of the aspects that has to be kept in mind considering the factas something is believable, it seems possible, real, or true that fake news detection is not only a simple web interface but also a quite complex thing that includes a lot of backend work to identify the information or reports about recent events.

Quantitative 1H NMR with global spectral deconvolution approach for quality assessment of natural and cultured Cordyceps sinensis

Cordyceps sinensis is a precious medicinal food which has been successfully cultivated indoors. It remains to be investigated for a simultaneous comparison on aqueous components of natural and cultivated samples. Herein, an approach of quantitative nuclear magnetic resonance (qNMR) analysis combined with global spectral deconvolution (GSD) was established for simultaneous quantification of 26 aqueous components in C. sinensis. Processed by GSD, the distorted baselines of 1H NMR spectra were greatly improved, and overlapped signals were also well separated so as to achieve accurate identification and quantitation of components in C. sinensis. Method validation by UHPLC-QTOF-MS and TOF-SIMS analysis revealed that qNMR combined with GSD is a reliable approach for simultaneous quantification of multiple components including characteristic markers of glutamine, GABA and trehalose in authentic and fake C. sinensis. The well-established qNMR approach can be used for quality assessment of natural and cultivated C. sinensis as well as differentiation from fake ones.

An explainable ensemble of multi-view deep learning model for fake review detection

Online reviews significantly impact consumers who are purchasing or seeking services via the Internet. Businesses and review platforms need to manage these online reviews to avoid misleading customers through fake ones. This necessitates developing intelligent solutions to detect these fake reviews and prevent their negative impact on businesses and customers. Therefore, many fake review detection models have been proposed to help distinguish fake reviews from genuine ones. However, these techniques depend on a limited perspective of features, mainly review content, to detect fake reviews, leading to poor performance in discovering the new patterns of fake review content and the dynamic behaviour of spammers. Therefore, there is still a need to develop new solutions to detect the new patterns of fake reviews. Hence, this paper proposes an explainable multi-view deep learning model to identify fake reviews based on different feature perspectives and classifiers. The proposed model can extract essential features from different perspectives, including review content, reviewer data, and product description. Moreover, we employ an ensemble approach that combines three popular deep learning algorithms: Bi-LSTM, CNN, and DNN, to enhance the performance of the fake review detection model. The results of two real-life datasets presented demonstrated the efficiency of our proposed model, where it outperformed the state-of-the-art methods with improvements ranging from 1% to 7% in terms of the AUC metric. To provide visibility into the outcomes of our proposed model and demonstrate the trust and transparency in the obtained results, we also offer a comprehensive explanation for our model results using Shapely Additive Explanations (SHAP) method and attention techniques. The experimental results prove that our proposed model can provide reasonable explanations that help users understand why specific reviews are classified as fake.

Multi-Person Pose Estimation in the Wild: Using Adversarial Method to Train a Top-Down Pose Estimation Network

Recent studies estimate human anatomical key points through the single monocular image, in which multichannel heatmaps are the key factor in determining the quality of human pose estimation. Multichannel heatmaps can efficiently handle the image-to-coordinate mapping task and the processing of semantic features. Most methods ignore physical constraints and internal relationships of human body parts, which easily misclassify left and right symmetrical parts as similar features. Some studies use RNNs on the top to incorporate priors about the structure of pose components and body configuration. Therefore, a novel top-down convolutional network is proposed to consider these priors during training, which can improve the robustness under complex field conditions in the wild. In order to learn the prior knowledge of human pose configuration, the hierarchy of fully convolutional networks (discriminator) is used to distinguish real poses from fake ones. Consequently, the pose network is inclined to make a pose estimation that the discriminator misjudges as true, which is reasonable in complex situations. The performance of the method is experimentally validated by pose estimation on the MS COCO human key point detection task. The proposed approach outperforms the original method and generates robust pose predictions, demonstrating efficiency by using adversarial learning.

Fusion Biometric Recognition Using CNN and LSTM Algorithms

Abstract: The automatic identification of people using their unique physical characteristics for security purposes is known as biometrics. Biometric authentication has a significant difficulty that calls for the development of more efficient methods in order to confirm the actual presence of a true legitimate trait as opposed to a fake self-manufactured synthetic or reconstructed sample. Recent developments in machine learning, computer vision, and pattern recognition have accelerated the development of the biometric recognition technology. The suggested approach intends to strengthen the security of biometric recognition frameworks by introducing two authentication. Each modality faces a unique set of difficulties. When practical, adults were used to evaluate the performance of technology and software created for infants. The type of biometric recognition technology will determine how accurate it is the effectiveness of the algorithm, the biometric trait used, and the calibre of the data collected. The recommended method beats earlier state-of-the-art approaches, and extra biometric data reveals extremely valuable information that may be used to quite efficiently discriminate actual features from fake ones

Global–Local Facial Fusion Based GAN Generated Fake Face Detection

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What's more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global-local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods.

Real and Altered Fingerprint Classification Based on Various Features and燙lassifiers

Biometric recognition refers to the identification of individuals through their unique behavioral features (e.g., fingerprint, face, and iris). We need distinguishing characteristics to identify people, such as fingerprints, which are world-renowned as the most reliable method to identify people. The recognition of fingerprints has become a standard procedure in forensics, and different techniques are available for this purpose. Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models. Therefore, we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones. In order to improve fingerprint classification accuracy, our proposed method used the most effective texture features and classifiers. Discriminant Analysis (DCA) and Gaussian Discriminant Analysis (GDA) are employed as classifiers, along with Histogram of Oriented Gradient (HOG) and Segmentation-based Feature Texture Analysis (SFTA) feature vectors as inputs. The performance of the classifiers is determined by assessing a range of feature sets, and the most accurate results are obtained. The proposed method is tested using a Sokoto Coventry Fingerprint Dataset (SOCOFing). The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times. Three distinct degrees of obliteration, central rotation, and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints. The proposal achieved massive success with a classification accuracy reaching 99%. The experimental results indicate that the proposed method for fingerprint classification is feasible and effective. The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.

Blockchain-Based Solution for Detecting and Preventing Fake Check Scams

Fake check scam is one of the most common attacks used to commit fraud against consumers. This fraud is particularly costly for victims because they generally lose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 h to detect the scam. In this context, we propose a blockchain-based scheme to authenticate checks and detect fake check scams. Moreover, our approach allows the revocation of used checks. More precisely, our approach helps the banks to share information about provided checks and used ones, without exposing the banks’ customers’ personal data. We demonstrate a proof of concept of our proposed approach using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Namecoin</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hyperledger</i> blockchain technologies.

Curbing Misinformation / Fake News Through Media Literacy Education of Students in Nigeria

Social media is a major source of information among both adults and young people. It is essential in the quick dissemination of information. Through social media people receive educational, health, social, economic, and political news which help them to make informed decisions. However, it is unfortunate to note that some information/news in social media are fake news and have caused fear, death, riot and disharmony among various ethnic groups. It is believed that because of the high level of confidence in information on social media, there is the likelihood of sharing the information without authentication. This lacuna/loophole has resulted in unfortunate situations. People have knowingly or unknowingly shared fake news that have created problems and making of wrong decisions that have disastrous consequences on the people. It has also caused lack of trust in government activities, hate speech, riots, and mayhem. It has also made the youths develop unwholesome habits like taking hard drugs, self-medication, joining cults, dropping out of school, depression, and suicide. It has become necessary to teach youths how to distinguish correct news from fake ones for their well-being and the development of the country.

A Domain Generalization Method Based on Hybrid Meta-Learning for Face Anti-Spoofing

&lt;p&gt;For face anti-spoofing, many methods have been proposed to improve the security of face recognition systems. Due to distribution discrepancies among different domains, it is difficult to seek a generalized space which can generalize well to unseen attacks. In this paper, we propose a framework based on meta-learning method to improve the generalization ability of face anti-spoofing. The feature extractor is trained with forcing the distribution of real faces more compact while the distribution of fake faces is more dispersed among domains. Then we add a hybrid-domain meta learner module to simulate multiple domain shift scenarios. Moreover, we add a refined triplet mining to constrain the distance between real faces and fake ones. Multiple gradient information is integrated to optimize the feature extractor and train the model with good generalization performance to unseen attacks of various scenarios. Extensive experiments on four public datasets show that our proposed method can get better generalization ability to unseen target domain compared with state-of-the-art methods. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Corona Virus (COVID-19) Pandemic Knowledge, Perceptions and Practices among Nigerian Academic Communities

Abstract: Purpose: This study assesses Nigerian academic communities’ (NAC) knowledge, perception, and practices regarding the COVID-19 pandemic. Research Methodology: Online snowball sampling technique with 5-point Likert scale online questionnaire was used to assess NAC knowledge, perception, and practices amidst coronavirus pandemic. About 368 participants comprised of academic and non-academic staff from north central geopolitical zone of Nigeria participated in the study. Responses were analysed using the SPSS. Result: NAC gathered, shared, and updated their knowledge about COVID-19 mostly through social media. Participants professed social media as the most effective platform for education and updates on COVID-19 compared to the conventional media and government sources. Despite that, participants were discovered to have slight issues in differentiating real news from fake ones. The participants reasonably practised precautionary measures as directed by the government and centre for disease control. Limitations: This study is limited to academic environment while COVID-19 affects all sundries and sectors of the economy respectively. Contribution: This study contributes to the knowledge and insight of COVID-19 among NAC and suggests means of managing the virus to avoid further spread. Keywords: 1. Coronavirus 2. COVID-19 3. Pandemic 4. Nigerian Academic Communities 5. Perception