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Journal articles on the topic 'Explicit content detection'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Marcial Basilio, Jorge Alberto, Gualberto Aguilar Torres, Gabriel Sanchez Perez, Linda Karina Toscano Medina, Hector Manuel Perez Meana, and Enrique Escamilla Hernadez. "Explicit Content Image Detection." Signal & Image Processing : An International Journal 1, no. 2 (2010): 47–58. http://dx.doi.org/10.5121/sipij.2010.1205.

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Qamar Bhatti, Ali, Muhammad Umer, Syed Hasan Adil, Mansoor Ebrahim, Daniyal Nawaz, and Faizan Ahmed. "Explicit Content Detection System: An Approach towards a Safe and Ethical Environment." Applied Computational Intelligence and Soft Computing 2018 (July 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/1463546.

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An explicit content detection (ECD) system to detect Not Suitable For Work (NSFW) media (i.e., image/ video) content is proposed. The proposed ECD system is based on residual network (i.e., deep learning model) which returns a probability to indicate the explicitness in media content. The value is further compared with a defined threshold to decide whether the content is explicit or nonexplicit. The proposed system not only differentiates between explicit/nonexplicit contents but also indicates the degree of explicitness in any media content, i.e., high, medium, or low. In addition, the system

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Appati, Justice Kwame, Kennedy Yaw Lodonu, and Richmond Chris-Koka. "A Review of Image Analysis Techniques for Adult Content Detection." International Journal of Software Innovation 9, no. 2 (2021): 102–21. http://dx.doi.org/10.4018/ijsi.2021040106.

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The fast growth of internet access globally without boundary has led to some negative impacts among children who are exposed to pornographic contents daily. Many parental control strategies have been put in place to protect these children; however, these strategies are usually inspired by political and social interventions. With the availability of computational tools, many automated explicit content detection methods though having their flaws have been proposed to support these social interventions. In this study, a review of the current automated adult content detectors is presented with ope

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Chen, Xiaoyuan, Turki Aljrees, Muhammad Umer, et al. "A novel approach for explicit song lyrics detection using machine and deep ensemble learning models." PeerJ Computer Science 9 (August 30, 2023): e1469. http://dx.doi.org/10.7717/peerj-cs.1469.

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The content of music is not always suitable for all ages. Industries that manage music content are looking for ways to help adults determine what is appropriate for children. Lyrics of songs have become increasingly inappropriate for kids and can negatively impact their mental development. However, it is difficult to filter explicit musical content because it is mostly done manually, which is time-consuming and prone to errors. Existing approaches lack the desired accuracy and are complex. This study suggests using a combination of machine learning and deep learning models to automatically scr

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Muhammad Fadzli, Muhammad Arif Haikal, Mohd Fadzil Abu Hassan, and Norazlin Ibrahim. "Explicit kissing scene detection in cartoon using convolutional long short-term memory." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 213–20. http://dx.doi.org/10.11591/eei.v11i1.3542.

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The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learning-based classifier to detect the kissing scene in the cartoon by using Da

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Muhammad, Arif Haikal Muhammad Fadzli, Fadzil Abu Hassan Mohd, and Ibrahim Norazlin. "Explicit kissing scene detection in cartoon using convolutional long short-term memory." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 213–20. https://doi.org/10.11591/eei.v11i1.3542.

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The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learningbased classifier to detect the kissing scene in the cartoon by usi

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Marcial Basilio, Jorge A., Gualberto Aguilar Torres, Gabriel Sánchez Pérez, Karina Toscano Medina, and Héctor M. Pérez Meana. "Novel method for pornographic image detection using HSV and YCbCr color models." Revista Facultad de Ingeniería Universidad de Antioquia, no. 64 (October 3, 2012): 79–90. http://dx.doi.org/10.17533/udea.redin.13117.

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In this paper a novel method to explicit content or pornographic images detection is proposed, using the transformation from RGB to HSV or YCbCr color model, which is the most usual format to images that exists on Internet, moreover the using of a threshold to skin detection applying the color models HSV and YCbCr is proposed. Using the proposed threshold the image is segmented, once the image segmented, the skin quantity localized in that image is calculated. The obtained results using the proposed system are compared with two programs which carry out with the same goal, the Forensic Toolkit

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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew

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Meguellati, Elyas, Assaad Zeghina, Shazia Sadiq, and Gianluca Demartini. "LLM-Based Semantic Augmentation for Harmful Content Detection." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 1190–209. https://doi.org/10.1609/icwsm.v19i1.35868.

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Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-ri

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Shahriari, Mohsen, Sabrina Haefele, and Ralf Klamma. "Using Content to Identify Overlapping Communities in Question Answer Forums." JUCS - Journal of Universal Computer Science 23, no. (9) (2017): 907–31. https://doi.org/10.3217/jucs-023-09-0907.

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Nowadays, people use online social networks almost every day. They activate either due to their interests, or to search or catch their desirable information. Users of online social networks generate structural and contextual traces that can be analyzed by, i.e., network science researchers. Researchers can describe networks fabricated out of online traces from different perspectives that one of them is communities. Overlapping communities are overlapped structures, in which nodes have denser connections with each other than the rest of the network. Different approaches have addressed this prob

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Hashir, Muhammad Haseeb, Memoona, and Sung Won Kim. "TARGE: large language model-powered explainable hate speech detection." PeerJ Computer Science 11 (May 30, 2025): e2911. https://doi.org/10.7717/peerj-cs.2911.

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The proliferation of user-generated content on social networking sites has intensified the challenge of accurately and efficiently detecting inflammatory and discriminatory speech at scale. Traditional manual moderation methods are impractical due to the sheer volume and complexity of online discourse, necessitating automated solutions. However, existing deep learning models for hate speech detection typically function as black-box systems, providing binary classifications without interpretable insights into their decision-making processes. This opacity significantly limits their practical uti

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Bhasme, Amisha Subhashrao. "Generative AI for Ethical and Bias-Free Content Moderation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41387.

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The growth of online platforms has led to an increase in harmful content, such as hate speech, fake news, and explicit images. While traditional content moderation techniques are human-centric, they struggle to scale effectively. Generative AI presents an opportunity to automate and enhance content moderation, offering efficiency at scale. However, generative AI models must be designed to detect harmful content while ensuring fairness and ethical behavior, avoiding biases and over-censorship. This paper explores the challenges of using generative AI for content moderation, focusing on bias det

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Sallahudin, Sultan, Muhammad Ismail, Subhan Ali, Aftab Ahmed, and Muhammad Faizan Hameed. "SafeCon: AI-Powered Real-Time Cyber Grooming Detection System." VFAST Transactions on Software Engineering 13, no. 2 (2025): 44–55. https://doi.org/10.21015/vtse.v13i2.2118.

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In recent times, the rise in online communication has unfortunately led to a significant increase in harmful activities. Countless instances involve people, especially children, becoming victims of distressing experiences like online sexual conversation. Reports suggest that a substantial number of young individuals, approximately one in four, have encountered online harassment or inappropriate content. Additionally, there has been a disturbing surge in cases involving the exploitation of children through grooming and exposure to explicit content. Leveraging the PAN12 dataset, we employ the Un

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Zhang, Linhao, Li Jin, Xian Sun, et al. "TOT：Topology-Aware Optimal Transport for Multimodal Hate Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4884–92. http://dx.doi.org/10.1609/aaai.v37i4.25614.

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Multimodal hate detection, which aims to identify the harmful content online such as memes, is crucial for building a wholesome internet environment. Previous work has made enlightening exploration in detecting explicit hate remarks. However, most of their approaches neglect the analysis of implicit harm, which is particularly challenging as explicit text markers and demographic visual cues are often twisted or missing. The leveraged cross-modal attention mechanisms also suffer from the distributional modality gap and lack logical interpretability. To address these semantic gap issues, we prop

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Bekaryan, Lilit. "Lost in “Transl-Hation”: Exploring the Impact of Machine Translation as an Intermediary Tool in Detecting Armenian Hate Speech." Translation Studies: Theory and Practice 3, no. 2 (6) (2023): 40–47. http://dx.doi.org/10.46991/tstp/2023.3.2.040.

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As the pervasive spread of hate speech continues to pose significant challenges to online communities, detecting, and countering hateful content on social media has become a priority. Social media platforms typically use machine translation to identify the hateful content of the posts made in languages other than English. If this approach works effectively in identifying explicit hateful content in languages that are predominantly used on social media, its effect is almost insignificant when it comes to Armenian. The present research investigates the effectiveness of machine translation as an

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FOUCAMBERT, DENIS, and JACQUES BAILLÉ. "Evolution of the missing-letter effect among young readers between ages 5 and 8." Applied Psycholinguistics 32, no. 1 (2010): 1–17. http://dx.doi.org/10.1017/s0142716410000263.

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ABSTRACTIn light of the numerous studies on the detection of target letters among adults, it is generally accepted that the missing-letter effect depends both on a given word's frequency in its language and on its role (function vs. content) in a sentence. Following a presentation of several models explaining these observations we analyze the results of a letter-detection task given to 886 French students from kindergarten to second grade. The purpose of the present study is to determine the moment when the sensitivity to content/function word distinction emerges. The results of this study rev

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Muniappan, Ramaraj, Dhendapani Sabareeswaran, Chembath Jothish, et al. "Optimizing feature extraction for tampering image detection using deep learning approaches." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1853. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1853-1864.

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Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, h

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Ramaraj, Muniappan Dhendapani Sabareeswaran Chembath Jothish Joe Arun Raja Srividhya Selvaraj Thangarasu Nainan Bhaarathi Ilango Dhinakaran Sumbramanian. "Optimizing feature extraction for tampering image detection using deep learning approaches." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1853–64. https://doi.org/10.11591/ijeecs.v35.i3.pp1853-1864.

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Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, h

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Cela-Conde, Camilo J., Gisèle Marty, Enric Munar, Marcos Nadal, and Lucrecia Burges. "The “Style Scheme” Grounds Perception of Paintings." Perceptual and Motor Skills 95, no. 1 (2002): 91–100. http://dx.doi.org/10.2466/pms.2002.95.1.91.

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We studied the formation of style scheme (identification of the style that characterizes an artist) presenting 100 participants aesthetic visual stimuli. Participants were Spanish university students who volunteered: 72 women, 28 men of mean age 22.8 yr. Among those 50 were enrolled in History of Art and 50 students in Psychology. Stimuli belonged to different categories—High Art (pictures of well-known artists, like Van Gogh)/Popular Art (decorative pictures like Christmas postcards) and Representational (pictures with explicit meaning content, like a landscape)/Abstract (pictures without exp

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Fu, Qiang, Zonglei Jing, Zonghao Ying, and Xiaoqian Li. "PRJ: Perception–Retrieval–Judgement for Generated Images." Electronics 14, no. 12 (2025): 2354. https://doi.org/10.3390/electronics14122354.

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The rapid progress of generative AI has enabled remarkable creative capabilities, yet it also raises urgent concerns regarding the safety of AI-generated visual content in real-world applications such as content moderation, platform governance, and digital media regulation. This includes unsafe material such as sexually explicit images, violent scenes, hate symbols, propaganda, and unauthorized imitations of copyrighted artworks. Existing image safety systems often rely on rigid category filters and produce binary outputs, lacking the capacity to interpret context or reason about nuanced, adve

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Namazbayev, Almas. "ANALYSIS OF NLP METHODS TO IDENTIFY OFFENSIVELANGUAGE." Suleyman Demirel University Bulletin Natural and Technical Sciences 64, no. 1 (2024): 112–22. https://doi.org/10.47344/sdubnts.v64i1.1181.

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This research focuses on the application of Natural LanguageProcessing (NLP) techniques to detect offensive language in textual data aimed at improving content moderation on digital communication platforms. Using a dataset, the study evaluates the effectiveness of advanced NLP models and algorithms in detecting explicit and implicit forms of offensive language. The core of the analysis centers around transformer-based models, in particularBERT (Bidirectional Encoder Representations from Transformers). The study addresses the challenges of offensive expression detection, highlighting both the s

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RAMAKRISHNAN, Mr R. "PROTECTING USERS FROM ONLINE HARASSMENT THROUGH AUTOMATED DETECTION SYSTEMS." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04514.

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ABSTRACT: Cyberbullying is a behavior sometimes unique to electronic media such as social media, messaging apps, and online games, and refers to the digital harassment or harm of individuals. Cyberbullying can be particularly damaging emotionally because when private or damaging content is released or made public, it may become permanent and astonishingly this action ultimately harms not only the person's behavior, but also their reputation and image. In many contexts cyberbullying can take the form of online insults, hostility, or even the implicit or explicit release of personal information

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Babu, Ramesh, S. Ramakrishna, and Suneel Kumar Duvvuri. "Hybrid NLP framework for enhanced sentiment analysis ‎and topic detection on YouTube." International Journal of Basic and Applied Sciences 14, no. 1 (2025): 304–13. https://doi.org/10.14419/kft8ae18.

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This paper introduces an advanced hybrid NLP framework designed to enhance sentiment analysis and topic detection in YouTube ‎comments. By combining feature extraction methods like Bag of Words (BoW) and TF-IDF with neural network models like LSTM and Bi-LSTM, the framework effectively uncovers latent topics and sentiment orientations. The study specifically analyzes comments on Oscar-nominated movie trailers, demonstrating the framework's ability to capture both explicit and implicit patterns of sentiment. This study shows ‎that the Bi-LSTM model with BoW features achieves the highest perform

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Malkawi, Rami, Mohammad Daradkeh, Ammar El-Hassan, and Pavel Petrov. "A Semantic Similarity-Based Identification Method for Implicit Citation Functions and Sentiments Information." Information 13, no. 11 (2022): 546. http://dx.doi.org/10.3390/info13110546.

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Automated citation analysis is becoming increasingly important in assessing the scientific quality of publications and identifying patterns of collaboration among researchers. However, little attention has been paid to analyzing the scientific content of the citation context. This study presents an unsupervised citation detection method that uses semantic similarities between citations and candidate sentences to identify implicit citations, determine their functions, and analyze their sentiments. We propose different document vector models based on TF-IDF weights and word vectors and compare t

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Seyler, Dominic, Shulong Tan, Dingcheng Li, Jingyuan Zhang, and Ping Li. "Textual Analysis and Timely Detection of Suspended Social Media Accounts." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 644–55. http://dx.doi.org/10.1609/icwsm.v15i1.18091.

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Suspended accounts are high-risk accounts that violate the rules of a social network. These accounts contain spam, offensive and explicit language, among others, and are incredibly variable in terms of textual content. In this work, we perform a detailed linguistic and statistical analysis into the textual information of suspended accounts and show how insights from our study significantly improve a deep-learning-based detection framework. Moreover, we investigate the utility of advanced topic modeling for the automatic creation of word lists that can discriminate suspended from regular accoun

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Despot, Kristina Š., Ana Ostroški Anić, and Tony Veale. "“Somewhere along your pedigree, a bitch got over the wall!” A proposal of implicitly offensive language typology." Lodz Papers in Pragmatics 19, no. 2 (2023): 385–414. http://dx.doi.org/10.1515/lpp-2023-0019.

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Abstract The automatic detection of implicitly offensive language is a challenge for NLP, as such language is subtle, contextual, and plausibly deniable, but it is becoming increasingly important with the wider use of large language models to generate human-quality texts. This study argues that current difficulties in detecting implicit offence are exacerbated by multiple factors: (a) inadequate definitions of implicit and explicit offense; (b) an insufficient typology of implicit offence; and (c) a dearth of detailed analysis of implicitly offensive linguistic data. In this study, based on a

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Lavie, Nilli, Diane M. Beck, and Nikos Konstantinou. "Blinded by the load: attention, awareness and the role of perceptual load." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1641 (2014): 20130205. http://dx.doi.org/10.1098/rstb.2013.0205.

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What is the relationship between attention and conscious awareness? Awareness sometimes appears to be restricted to the contents of focused attention, yet at other times irrelevant distractors will dominate awareness. This contradictory relationship has also been reflected in an abundance of discrepant research findings leading to an enduring controversy in cognitive psychology. Lavie's load theory of attention suggests that the puzzle can be solved by considering the role of perceptual load. Although distractors will intrude upon awareness in conditions of low load, awareness will be restrict

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Jakku, Sai Sreekar, Sudheer Narla, Abhinav Reddy Emmadi, and V. Kakulapati. "A Novel Approach to Detection of Fake News in Online Communities." Advances in Research 24, no. 4 (2023): 79–84. http://dx.doi.org/10.9734/air/2023/v24i4950.

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Fake news serving various political and commercial agendas has emerged on the web and spread rapidly in recent years, thanks in large part to the proliferation of online social networks. People who use informal online groups are especially vulnerable to the sneaky effects of deceptive language used in fake news on the internet, which has far-reaching effects on real society. To make information in informal online communities more reliable, it is important to be able to spot fake news as soon as possible. The goal of this study is to look at the criteria, methods, and calculations that are used

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Taib, Eva Nauli, Rostina Taib, and Evinopita Taib. "Content Analysis of Biology Learning Objectives in Merdeka Curriculum: Detection of Misconceptions and Terminological Interference from Predecessor." Jurnal Penelitian Pendidikan IPA 11, no. 6 (2025): 594–609. https://doi.org/10.29303/jppipa.v11i6.11531.

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Indonesia's shift from Curriculum 2013 to the Merdeka Curriculum has introduced significant challenges in biology education, particularly in formulating competency-based learning objectives. This study analyzes professional misconceptions in learning objectives using qualitative content analysis of eight curriculum documents—lesson plans and teaching modules—focused on "Environmental Change" from three provinces (Aceh, North Sumatra, Bangka Belitung). Contributors included senior teachers, junior teachers, and pre-service teachers under mentor guidance. Using Webb's curriculum alignment method

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Buchner, Jens S., Ute Wollschläger, and Kurt Roth. "Inverting surface GPR data using FDTD simulation and automatic detection of reflections to estimate subsurface water content and geometry." GEOPHYSICS 77, no. 4 (2012): H45—H55. http://dx.doi.org/10.1190/geo2011-0467.1.

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A new inversion scheme for common-offset ground-penetrating radar measurements at multiple antenna separations was proposed, which is intermediate between inverting of picked reflectors using ray-tracing and full-waveform inversion. The measurements are modeled similarly to the real data using 2D finite-difference time-domain simulations. These simulations are obtained with a parameterized model of the subsurface that consists of several layers with constant dielectric permittivity and an explicit representation of the layers’ interfaces. Then, reflections in the modeled and in the real data a

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Peng, Jin, Chengming Liu, Haibo Pang, Xiaomeng Gao, Guozhen Cheng, and Bing Hao. "GP-Net: Image Manipulation Detection and Localization via Long-Range Modeling and Transformers." Applied Sciences 13, no. 21 (2023): 12053. http://dx.doi.org/10.3390/app132112053.

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With the rise of image manipulation techniques, an increasing number of individuals find it easy to manipulate image content. Undoubtedly, this presents a significant challenge to the integrity of multimedia data, thereby fueling the advancement of image forgery detection research. A majority of current methods employ convolutional neural networks (CNNs) for image manipulation localization, yielding promising outcomes. Nevertheless, CNN-based approaches possess limitations in establishing explicit long-range relationships. Consequently, addressing the image manipulation localization task neces

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Hu, Weiqi, Ye Wang, Yan Jia, Qing Liao, and Bin Zhou. "A Multi-modal Prompt Learning Framework for Early Detection of Fake News." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 651–62. http://dx.doi.org/10.1609/icwsm.v18i1.31341.

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Information spreads quickly through social media platforms, especially fake news with negative or even malicious intentions. In recent years, psychological studies have found that explicit reminders of fake news would diminish its consequence. Therefore, it is crucial to identify their authenticity at an early stage to avoid serious consequences. However, existing methods for fake news detection either utilize auxiliary information including users’ profiles and related events propagation networks or require sufficient and high-quality training data, which is not suitable for early fake news de

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Wen, Hamilton, Janos L. Mathe, Stuart T. Weinberg, Asli Ozdas Weitkamp, and Scott D. Nelson. "Creating an immunization content database for knowledge management across clinical systems." American Journal of Health-System Pharmacy 76, Supplement_3 (2019): S79—S84. http://dx.doi.org/10.1093/ajhp/zxz134.

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Abstract Purpose A initiative at an academic medical center to create a single database of immunization-related content to inform the build and configuration of immunization-related knowledge assets across multiple clinical systems is described. Methods Semistructured expert interviews were conducted to ascertain the immunization information needs of the institution’s clinical systems. Based on those needs, an immunization domain model constructed with data available from the Centers for Disease Control and Prevention (CDC) website was developed and used to analyze and compare current immuniza

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Alam, Tanweer, and Ruchi Gupta. "Reviewing the Framework of Blockchain in Fake News Detection." Jurnal Online Informatika 9, no. 2 (2025): 286–96. https://doi.org/10.15575/join.v9i2.1349.

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In the social media environment, fake news is a significant issue. It might be online or offline, depending on the field of journalism. Concerns have been expressed by media and publishing houses, who are looking for solutions to the problem. One of the solutions the industry has to offer in this area is Blockchain. It could be digital security trading, source or identity verification, or quotes following a certain news piece, photo, or video. It's miles of shared document generation to deliver timely files, and it's done with the help of a specific article, video, or image that has been addre

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Sui Lyn, Hor, Sarina Mansor, Nouar AlDahoul, and Hezerul Abdul Karim. "Convolutional Neural Network-based Transfer Learning and Classification of Visual Contents for Film Censorship." Journal of Engineering Technology and Applied Physics 2, no. 2 (2020): 28–35. http://dx.doi.org/10.33093/jetap.2020.2.2.5.

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Content filtering is gaining popularity due to easy exposure of explicit visual contents to the public. Excessive exposure of inappropriate visual contents can cause devastating effects such as the growth of improper mindset and rise of societal issues such as free sex, child abandonment and rape cases. At present, most of the broadcasting media sites are hiring censorship editors to label graphic contents manually. Nevertheless, the efficiency is limited by factors such as the attention span of humans and the training required for the editors. This paper proposes to study the effect of usage

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Law Kian Seng, NORMAISHARAH MAMAT, Hafiza Abas, and Wan Noor Hamiza Wan Ali. "AI Integrity Solutions for Deepfake Identification and Prevention." Open International Journal of Informatics 12, no. 1 (2024): 35–46. http://dx.doi.org/10.11113/oiji2024.12n1.297.

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The increasing complexity of deepfake technology has sparked significant worries over individual privacy, the spread of false information, and deficiencies in cybersecurity. Deepfakes have the ability to effectively modify audio and visual content, resulting in a growing challenge to differentiate between real and fake content. To address this critical challenge, the study is conducting a survey to reveal a broad range of perspectives on the familiarity, encounters, and concerns related to deepfake technology. In addition, the study evaluates the effectiveness of current strategies in addressi

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Indre, Aditi, Jayesh Shinde, and Dr Srivaramangai Ramanujam. "The Impact of Deepfakes on Digital Media Authenticity." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2294–302. https://doi.org/10.22214/ijraset.2025.67796.

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Abstract: With digital manipulation blurring the lines between reality and fabrication, deepfakes have become one of the most shocking threats to media authenticity and public trust. Fed by advanced artificial intelligence techniques like Generative Adversarial Networks (GANs) and deep learning, deepfakes can create hyper-realistic videos, images, and audio that convincingly imitate real people. These forgeries have far-reaching implications, from spreading political misinformation and financial fraud to non-consensual explicit content and identity theft. As deepfake technology becomes more so

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Kumar, Shubham. "Smart System to Detect Adult Content and Child Pornography on Web." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1704–6. http://dx.doi.org/10.22214/ijraset.2021.38256.

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Abstract: Adult contents on the internet is very common today but it has become a serious concern now because of many reasons such as the proliferation of free to view adult websites has made it easier for individuals of any age to gain access to explicit content. Children increasingly use mobile devices such as smartphones to access the internet and these adult content can have bad impact on their mind. Excessive exposure to these contents can lead to addiction which can have very adverse effect on their mind and their heath.so i came up with an idea to reduce this activity on internet saving

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Ziems, Caleb, Ymir Vigfusson, and Fred Morstatter. "Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 808–19. http://dx.doi.org/10.1609/icwsm.v14i1.7345.

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Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced p

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Formenton, M., G. Panegrossi, D. Casella, et al. "Using a cloud electrification model to study relationships between lightning activity and cloud microphysical structure." Natural Hazards and Earth System Sciences 13, no. 4 (2013): 1085–104. http://dx.doi.org/10.5194/nhess-13-1085-2013.

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Abstract. In this study a one-dimensional numerical cloud electrification model, called the Explicit Microphysics Thunderstorm Model (EMTM), is used to find quantitative relationships between the simulated electrical activity and microphysical properties in convective clouds. The model, based on an explicit microphysics scheme coupled to an ice–ice noninductive electrification scheme, allows us to interpret the connection of cloud microphysical structure with charge density distribution within the cloud, and to study the full evolution of the lightning activity (intracloud and cloud-to-ground)

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Mudler, Jan, Andreas Hördt, Dennis Kreith, et al. "Broadband spectral induced polarization for the detection of Permafrost and an approach to ice content estimation – a case study from Yakutia, Russia." Cryosphere 16, no. 11 (2022): 4727–44. http://dx.doi.org/10.5194/tc-16-4727-2022.

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Abstract. The reliable detection of subsurface ice using non-destructive geophysical methods is an important objective in permafrost research. The ice content of the frozen ground is an essential parameter for further interpretation, for example in terms of risk analysis and for the description of permafrost carbon feedback by thawing processes. The high-frequency induced polarization method (HFIP) enables the measurement of the frequency-dependent electrical conductivity and permittivity of the subsurface, in a frequency range between 100 Hz and 100 kHz. As the electrical permittivity of ice

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Alabrah, Amerah. "SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention." PeerJ Computer Science 11 (April 16, 2025): e2803. https://doi.org/10.7717/peerj-cs.2803.

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Image forgery is an increasing threat, fueling misinformation and potentially impacting legal decisions and everyday life. Detecting forged media, including images and videos, is crucial for preserving trust and integrity across various platforms. Common forgery techniques like copy-move and splicing require robust detection methods to identify tampered areas without explicit guidance. The previously proposed studies focused on a single type of forgery detection utilizing block-based and key-point feature selection-based classical machine learning (ML) approaches. Furthermore, applied deep lea

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Gong, Zheng. "Automated Video Title Generation for Mobile Learning Resources: A Deep Learning Approach with Educational Context Awareness." Advances in Mobile Learning Educational Research 5, no. 1 (2025): 1344–55. https://doi.org/10.25082/amler.2025.01.010.

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With the rapid growth of mobile learning platforms, short educational videos have emerged as a critical resource for learners. However, manually generating concise and pedagogically meaningful titles for these videos remains a time-consuming challenge. To address this issue, this study proposes a deep learning framework designed for automated video title generation in educational contexts. The framework integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and natural language processing (NLP) techniques, with explicit awareness of pedagogical relevance. The

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John-Africa, Elijah, and Victor T. Emmah. "Performance Evaluation of LSTM and RNN Models in the Detection of Email Spam Messages." European Journal of Information Technologies and Computer Science 2, no. 6 (2022): 24–30. http://dx.doi.org/10.24018/compute.2022.2.6.80.

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Email spam is an unwanted bulk message that is sent to a recipient’s email address without explicit consent from the recipient. This is usually considered a means of advertising and maximizing profit, especially with the increase in the usage of the internet for social networking, but can also be very frustrating and annoying to the recipients of these messages. Recent research has shown that about 14.7 billion spam messages are sent out every single day of which more than 45% of these messages are promotional sales content that the recipient did not specifically opt-in. This has gotten the at

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Kamimura, Ryotaro. "Progressive Feature Extraction with a Greedy Network-growing Algorithm." Complex Systems 14, no. 2 (2024): 127–53. http://dx.doi.org/10.25088/complexsystems.14.2.127.

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In this paper, a new information theoretic method called the greedy network-growing algorithm is proposed. The method is called "greedy," because a network with this algorithm grows while absorbing as much information as possible from outside. The method is based upon information theoretic competitive learning and can solve the fundamental problems inherent in competitive learning, such as the dead neurons and inappropriate number of neurons problems. The new model can grow networks by repeatedly maximizing information content and by gradually extracting salient features from input patterns. B

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Laio, F., P. Allamano, and P. Claps. "Exploiting the information content of hydrological ''outliers'' for goodness-of-fit testing." Hydrology and Earth System Sciences 14, no. 10 (2010): 1909–17. http://dx.doi.org/10.5194/hess-14-1909-2010.

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Abstract. Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the behavior of the hypothetical model, FX(x), in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, techni

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Chen, Yuxin. "The Accuracy and Biases of AI-Based Internet Censorship in China." Journal of Research in Social Science and Humanities 4, no. 2 (2025): 27–36. https://doi.org/10.56397/jrssh.2025.02.05.

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AI-driven censorship has become a central mechanism for controlling online discourse in China, allowing for rapid detection and suppression of politically sensitive content. This paper explores the accuracy and biases of AI-based internet censorship, focusing on its evolution from manual to automated moderation, its effectiveness in identifying dissent, and its systemic biases that reinforce government narratives. While AI models are highly efficient in filtering explicit political speech, they struggle with disguised dissent, satire, and coded language, leading to inconsistent enforcement and

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Zhao, Li, Tongyang Zhu, Chuang Wang, Feng Tian, and Hongge Yao. "Image Inpainting Algorithm Based on Structure-Guided Generative Adversarial Network." Mathematics 13, no. 15 (2025): 2370. https://doi.org/10.3390/math13152370.

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To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent struct

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МОЛЧАНОВА, МАРИНА. "МЕТОД КЛАСИФІКАЦІЇ ТЕКСТІВ ЗА ВМІСТОМ ПРОПАГАНДИ НЕЙРОМЕРЕЖЕВИМИ МОДЕЛЯМИ ГЛИБОКОГО НАВЧАННЯ". Herald of Khmelnytskyi National University. Technical sciences 341, № 5 (2024): 344–50. https://doi.org/10.31891/2307-5732-2024-341-5-51.

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The method for classifying texts by propaganda content by neural network models of deep learning is proposed, based on combining traditional recurrent neural networks with long-term memory with transformers, which can provide a deeper understanding of sequence and context in text content. The peculiarity of proposed method is that it allows detecting both explicit and hidden propaganda messages, based on combining the capabilities of traditional recurrent neural networks with long-term memory and neural networks-transformers, as well as using the mechanism of training text data augmentation, w

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Laio, F., P. Allamano, and P. Claps. "Exploiting the information content of hydrological "outliers" for goodness-of-fit testing." Hydrology and Earth System Sciences Discussions 7, no. 4 (2010): 4851–74. http://dx.doi.org/10.5194/hessd-7-4851-2010.

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Abstract. Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the the behavior of the hypothetical model, FX(x), in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, te

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