Search Results (11)

Phishing attacks continue to pose serious challenges to cybersecurity, with attackers constantly refining their methods to bypass detection systems. One particularly evasive technique is Zero-Font phishing, which involves the insertion of invisible or zero-sized characters into email content to deceive both users and traditional email filters. Because these characters are not visible to human readers but still processed by email systems, they can be used to evade detection by traditional email filters, obscuring malicious intent in ways that bypass basic content inspection. This study introduces a proactive phishing detection tool capable of identifying both traditional and Zero-Font phishing attempts. The proposed tool leverages a multi-layered security framework, combining structural inspection and machine learning-based classification to detect both traditional and Zero-Font phishing attempts. At its core, the system incorporates an advanced machine learning model trained on a well-established dataset comprising both phishing and legitimate emails. The model alone achieves an accuracy rate of up to 98.8%, contributing significantly to the overall effectiveness of the tool. This hybrid approach enhances the system’s robustness and detection accuracy across diverse phishing scenarios. The findings underscore the importance of multi-faceted detection mechanisms and contribute to the development of more resilient defenses in the ever-evolving landscape of cybersecurity threats. Full article

This research aimed to apply a risk management methodology to multiple surface water sources in urban and rural areas of a developing country. The applied methodology enabled the identification of hazards, classification, and the prioritization of risks at 21 collection points in the rural area and 9 collection points in the urban area. Both rural and urban areas exhibited common events with a high-risk level, such as human access (100% in urban areas and 90% in rural areas), climatic events, and inadequate collection structures (100% of points in both urban and rural areas). However, rural areas presented specific risks associated with animal husbandry (70% of points with high risk), a lack of monitoring, limited infrastructure (30% of points with high risk), and wildlife, including birds and worms (50% of points with high risk in rural areas and 10% in urban points). On the other hand, urban areas faced challenges related to vandalism and sabotage (high risk in 40% of points). Understanding these similarities and differences permits integrated risk management among the various stakeholders who can contribute to risk management within a watershed. Full article

Food marketing targeting children influences their choices and dietary habits, and mainly promotes food high in fat, sugar, and salt as well as ultra-processed food. The aim of this study was to assess the nutritional quality of food and beverages marketed to children over the age of 3 and available on the Swiss market. Products with at least one marketing technique targeting children on the packaging were selected from five food store chains. Three criteria to assess nutritional quality were used: (1) nutritional composition (using the Nutri-Score), (2) degree of processing (NOVA classification), and (3) compliance with the World Health Organization (WHO) Nutrient Profile Model (NPM). A total of 735 products were found and analyzed. The most common marketing techniques used were childish names/fonts (46.9%), special characters (39.6%), and children’s drawings (31.3%). Most products had a Nutri-Score of D or E (58.0%) and were ultra-processed (91.8%). Only 10.2% of products displayed the Nutri-Score. The least processed products generally had a better Nutri-Score (p < 0.001). Most products (92.8%) did not meet the criteria of the WHO NPM. Products that met the WHO NPM criteria, organic products, and products with a nutritional claim generally had a better Nutri-Score and were less processed (p_s < 0.05). Pre-packaged foods and beverages marketed to children in the Swiss market were mostly of poor nutritional quality. Public health measures should be adopted to improve the nutritional quality of foods marketed to children in Switzerland and restrict the marketing of unhealthy foods to children. Full article

Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as regular (Kai Shu), running (Xing Shu), official (Li Shu), and cursive (Cao Shu) scripts. Beginners often start with the regular script, advancing to more intricate styles like cursive. Each style, marked by unique historical calligraphy contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification is pivotal. This study introduces an innovative convolutional neural network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphy styles from the Tang dynasty (690–907 A.D.), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5–96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in the categorization of both font and artistic styles. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications. Full article

Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification. We conducted various experiments to demonstrate the robustness of contrastive image representation learning. First, we built a multilingual synthetic dataset for Chinese, English, and Korean fonts. Next, we trained the model using various contrastive loss functions, i.e., normalized temperature scaled cross-entropy loss, triplet loss, and supervised contrastive loss. We made some explicit changes to the approach of applying contrastive learning in the domain of font style classification by not applying any image augmentation. We compared the results with those of a fully supervised approach and achieved comparable results using contrastive learning with fewer annotated images and a smaller number of training epochs. In addition, we also evaluated the effect of applying different contrastive loss functions on training. Full article

With the increase of various media, fonts continue to be newly developed. In Korea, numerous ‘Hangul’ fonts are also being developed, and as a result, the need for research on determining the similarity between fonts is emerging. For example, when creating a document, the font to be used must be downloaded from each computing environment. However, this is a very cumbersome process. If there is a font that is not supported in the system, the above problem can be easily solved by recommending the most similar font that can replace it. According to this need, we conducted various prior studies for similar font recommendations. As a result, we developed a ‘stroke element’ that exists in each consonant and vowel in Korean font and developed a font recommendation model using a stroke element. However, there is a limitation in that the existing research was studied only for the structured fonts corresponding to the printed type. Additionally, the font size was not considered in the font recommendation. In this study, two experiments were conducted to expand the font recommendation model by supplementing the limitations of existing studies. First, in order to enable similar font recommendations based on the stroke element even in fonts with various shapes, the font was classified according to the shape, and the stroke elements in each classification were detected. Second, when the font sizes were different, the change in the font recommendations result based on the stroke element was analyzed. In conclusion, we found that it was necessary to find a plan to extract stroke elements for font recommendation of fonts that do not belong to standard fonts. In addition, since the influence of the stroke element varies depending on the size of the font, we propose a stroke element weight model that can be used for recommendation by reflecting it. Full article

Optical Character Recognition has made large strides in the field of recognizing printed and properly formatted text. However, the effort attributed to developing systems that are able to reliably apply OCR to both printed as well as handwritten text simultaneously, such as hand-filled forms, is lackadaisical. As Machine printed/typed text follows specific formats and fonts while handwritten texts are variable and non-uniform, it is very hard to classify and recognize using traditional OCR only. A pre-processing methodology employing semantic segmentation to identify, segment and crop boxes containing relevant text on a given image in order to improve the results of conventional online-available OCR engines is proposed here. In this paper, the authors have also provided a comparison of popular OCR engines like Microsoft Cognitive Services, Google Cloud Vision and AWS recognitions. We have proposed a pixel-wise classification technique to accurately identify the area of an image containing relevant text, to feed them to a conventional OCR engine in the hopes of improving the quality of the output. The proposed methodology also supports the digitization of mixed typed text documents with amended performance. The experimental study shows that the proposed pipeline architecture provides reliable and quality inputs through complex image preprocessing to Conventional OCR, which results in better accuracy and improved performance. Full article

In the field of computer vision, large-scale image classification tasks are both important and highly challenging. With the ongoing advances in deep learning and optical character recognition (OCR) technologies, neural networks designed to perform large-scale classification play an essential role in facilitating OCR systems. In this study, we developed an automatic OCR system designed to identify up to 13,070 large-scale printed Chinese characters by using deep learning neural networks and fine-tuning techniques. The proposed framework comprises four components, including training dataset synthesis and background simulation, image preprocessing and data augmentation, the process of training the model, and transfer learning. The training data synthesis procedure is composed of a character font generation step and a background simulation process. Three background models are proposed to simulate the factors of the background noise patterns on ID cards. To expand the diversity of the synthesized training dataset, rotation and zooming data augmentation are applied. A massive dataset comprising more than 19.6 million images was thus created to accommodate the variations in the input images and improve the learning capacity of the CNN model. Subsequently, we modified the GoogLeNet neural architecture by replacing the fully connected layer with a global average pooling layer to avoid overfitting caused by a massive amount of training data. Consequently, the number of model parameters was reduced. Finally, we employed the transfer learning technique to further refine the CNN model using a small number of real data samples. Experimental results show that the overall recognition performance of the proposed approach is significantly better than that of prior methods and thus demonstrate the effectiveness of proposed framework, which exhibited a recognition accuracy as high as 99.39% on the constructed real ID card dataset. Full article

Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model. Full article

This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers. Full article

Twenty-eight fungal feruloyl esterases (FAEs) were evaluated for their synthetic abilities in a ternary system of n-hexane: t-butanol: 100 mM MOPS-NaOH pH 6.0 forming detergentless microemulsions. Five main derivatives were synthesized, namely prenyl ferulate, prenyl caffeate, butyl ferulate, glyceryl ferulate, and l-arabinose ferulate, offering, in general, higher yields when more hydrophilic alcohol substitutions were used. Acetyl xylan esterase-related FAEs belonging to phylogenetic subfamilies (SF) 5 and 6 showed increased synthetic yields among tested enzymes. In particular, it was shown that FAEs belonging to SF6 generally transesterified aliphatic alcohols more efficiently while SF5 members preferred bulkier l-arabinose. Predicted surface properties and structural characteristics were correlated with the synthetic potential of selected tannase-related, acetyl-xylan-related, and lipase-related FAEs (SF1-2, -6, -7 members) based on homology modeling and small molecular docking simulations. Full article