406 research outputs found

    Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

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    Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models

    Face Recognition using the LCS algorithm

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    Today, the topic of human identification based on physical characteristics is a necessity in various fields. As a biometric system, a facial recognition system is fundamentally a pattern recognition system that identifies a person based on specific physiological or behavioral feature vectors. The feature vector is typically stored in a database upon extraction. The main objective of this research is to study and assess the effect of selecting the proper image attributes using the Cuckoo search algorithm. Thus, the selection of an optimal subset, given the large size of the feature vector dimensions to expedite the facial recognition algorithm is essential and substantial. Initially, by using the existing database, image characteristics are extracted and selected as a binary optimal subset of facial features using the Cuckoo algorithm. This subset of optimal features are evaluated by classifying nearest neighbor and neural networks. By calculating the accuracy of this classification, it is clear that the proposed method is of higher accuracy compared to previous methods in facial recognition based on the selection of significant features by the proposed algorithm

    Detection of AI-created images using pixel-wise feature extraction and convolutional neural networks

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    Generative AI has gained enormous interest nowadays due to new applications like ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a text prompt and are even able to create photorealistic images. Due to this fact, intense research has been performed to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deepfake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research is to detect photorealistic AI-created images versus real photos coming from a physical camera. Id est, making a binary decision over an image, asking whether it is artificially or naturally created. Artificial images do not need to try to represent any real object, person, or place. For this purpose, techniques that perform a pixel-level feature extraction are used. The first one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. The second one is error level analysis (ELA). This is another type of feature extraction traditionally used for detecting image editing. ELA is being used nowadays by photographers for the manual detection of AI-created images. Both kinds of features are used to train convolutional neural networks to differentiate between AI images and real photographs. Good results are obtained, achieving accuracy rates of over 95%. Both extraction methods are carefully assessed by computing precision/recall and F1-score measurements

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

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    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

    The Applications of Discrete Wavelet Transform in Image Processing: A Review

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    This paper reviews the newly published works on applying waves to image processing depending on the analysis of multiple solutions. the wavelet transformation reviewed in detail including wavelet function, integrated wavelet transformation, discrete wavelet transformation, rapid wavelet transformation, DWT properties, and DWT advantages. After reviewing the basics of wavelet transformation theory, various applications of wavelet are reviewed and multi-solution analysis, including image compression, image reduction, image optimization, and image watermark. In addition, we present the concept and theory of quadruple waves for the future progress of wavelet transform applications and quadruple solubility applications. The aim of this paper is to provide a wide-ranging review of the survey found able on wavelet-based image processing applications approaches. It will be beneficial for scholars to execute effective image processing applications approaches

    Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

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    In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, firstly, we investigate how 13 most popular SNs treat the uploaded pictures, in order to identify a possible implementation of image watermarking techniques by respective SNs. Secondly, on these 13 SNs, we test the robustness of several image watermarking algorithms. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is robust enough in spite of the fact that the pictures get downgraded during the uploading process by SNs. The results of our analysis on a real dataset of 8,400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs.Comment: 43 pages, 6 figure

    Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

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    In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features

    TP63 mutations are frequent in cutaneous melanoma, support UV etiology, but their role in melanomagenesis is unclear

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    In contrast to TP53, cancer development is rarely associated with mutations in the TP63 and TP73 genes. Recently, next generation sequencing analysis revealed that TP63 mutations are frequent, specifically in cutaneous melanomas. Cutaneous melanoma represents 4% of skin cancers but it is responsible for 80% of skin cancer related deaths. In the present study, we first determined whether all three members of the P53 family of transcription factors were found mutated in cutaneous melanomas by retrieving all TP53, TP63 and TP73 mutations from cBioPortal (http://www.cbioportal.org/). TP53 and TP63 were frequently mutated [15.0% (91/605) and 14.7% (89/605), respectively], while TP73 [1.5% (9/605)] was more rarely mutated (p<0.0001). A UV-mutation fingerprint was recognized for TP63 and TP73 genes. Then, we tried to evaluate the potential role of TP63 mutations as drivers or passengers in the tumorigenic process. In the former case, the amino acid substitutions should cause significant functional consequences on the main biochemical activity of the P63 protein, namely transactivation. The predicted effects of specific amino acid substitutions by two bioinformatics tools were rather different. Using a yeast-based functional assay, the observed hotspot mutant R379CP63 protein exhibited a substantial residual activity compared to the wild-type (>70%). This result does not support a major role of the mutant P63 protein in melanomagenesis while it is still consistent with the TP63 gene being a recorder of UV exposure. The TP63 mutation spectrum from cutaneous melanomas, when compared with that observed at the germinal level in patients affected by P63-associated diseases [ectodermal dysplasia syndromes, (EDs)], revealed significant differences. The TP63 mutations were more frequent at CpGs sites (p<0.0001) in EDs and at PyPy sites (p<0.0001) in cutaneous melanomas. The two spectra differed significantly (p<0.0001). We conclude that TP63 mutations are frequent in cutaneous melanoma, support UV etiology, but their role in melanomagenesis is unclear

    Characterisation of miR-494 in coagulation

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    Micro-ribonucleic acids (miRNAs) are non-coding RNAs that, in the majority of cases, regulate gene expression by binding to the 3’ untranslated region (UTR) sequence of target mRNAs to cause decreased mRNA levels. Recently, several studies showed that selected miRNAs also target important genes in coagulation and haemostasis. The miRNA, miR-494, downregulates Protein S (PS) mRNA levels by binding to the 3’UTR sequence of PROS1 mRNA transcript. Further study demonstrated that some coagulation factors without predicted miR-494 binding sites in their 3’UTR sequences showed significant changes in their mRNA levels, including plasminogen (PLG), tissue factor (F3) and C4BPα (C4BPA). It is possible that miR-494 directly targets transcriptional activators or repressors to indirectly regulate the expression of those coagulation factors lacking predicted miR-494 binding sites. This current study hypothesised that miR-494 has an important role in regulating coagulation pathways and haemostasis by targeting multiple coagulation factor genes via direct and indirect mechanisms. HuH-7 cells were transfected with miR-494 for 48h and 72h followed by mRNA and protein analysis. Direct interaction between miR-494 and transcription factor 3’UTRs (JUN, SP1 and STAT5B 3’UTRs) was determined using dual-luciferase reporter assays. The mRNA and protein levels of PS and PLG was significantly downregulated, and the C4BPA mRNA and protein levels were upregulated with the presence of miR-494. Moreover, the protein level of tissue factor (TF) was decreased at 72h post-transfection but no changes were found in its mRNA levels. Computational analyses showed that predicted miR-494 binding sites were found in the JUN, SP1 and STAT5B 3’UTRs. Dual luciferase reporter assay confirmed the presence of functional miR-494 binding sites in the 3’UTR sequence of SP1 and STAT5B. The SP1 and STAT5B mRNA levels were significantly downregulated with the presence of miR-494 but no change was observed for the JUN mRNA levels. Computational analysis showed that a predicted Sp1 binding site was found in the promoter region of human PLG gene. A report by Gutierrez-Fernandez et. al. (2007) suggested that Sp1 acts as a transcriptional activator in the murine PLG promoter. These suggested that miR-494 may indirectly downregulate PLG expression by targeting Sp1. Taken together, miR-494 directly downregulates PS expression, and indirectly downregulates PLG expression through Sp1 repression and upregulates C4BPA expression. These results suggested that miR-494 has a prothrombotic effect

    A framework for biometric recognition using non-ideal iris and face

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    Off-angle iris images are often captured in a non-cooperative environment. The distortion of the iris or pupil can decrease the segmentation quality as well as the data extracted thereafter. Moreover, iris with an off-angle of more than 30° can have non-recoverable features since the boundary cannot be properly localized. This usually becomes a factor of limited discriminant ability of the biometric features. Limitations also come from the noisy data arisen due to image burst, background error, or inappropriate camera pixel noise. To address the issues above, the aim of this study is to develop a framework which: (1) to improve the non-circular boundary localization, (2) to overcome the lost features, and (3) to detect and minimize the error caused by noisy data. Non-circular boundary issue is addressed through a combination of geometric calibration and direct least square ellipse that can geometrically restore, adjust, and scale up the distortion of circular shape to ellipse fitting. Further improvement comes in the form of an extraction method that combines Haar Wavelet and Neural Network to transform the iris features into wavelet coefficient representative of the relevant iris data. The non-recoverable features problem is resolved by proposing Weighted Score Level Fusion which integrates face and iris biometrics. This enhancement is done to give extra distinctive information to increase authentication accuracy rate. As for the noisy data issues, a modified Reed Solomon codes with error correction capability is proposed to decrease intra-class variations by eliminating the differences between enrollment and verification templates. The key contribution of this research is a new unified framework for high performance multimodal biometric recognition system. The framework has been tested with WVU, UBIRIS v.2, UTMIFM, ORL datasets, and achieved more than 99.8% accuracy compared to other existing methods
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