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Academic literature on the topic 'AI Generated Text Detection'

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Published: 28 December 2024

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Journal articles on the topic "AI Generated Text Detection"

Bhattacharjee, Amrita, and Huan Liu. "Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?" ACM SIGKDD Explorations Newsletter 25, no. 2 (March 26, 2024): 14–21. http://dx.doi.org/10.1145/3655103.3655106.

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Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zeroshot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI-generated or human-written text. Our findings provide insight on how ChatGPT and similar LLMs may be leveraged in automated detection pipelines by simply focusing on solving a specific aspect of the problem and deriving the rest from that solution. All code and data is available at https://github.com/AmritaBh/ChatGPT-as-Detector.

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Wang, Yu. "Survey for Detecting AI-generated Content." Advances in Engineering Technology Research 11, no. 1 (July 18, 2024): 643. http://dx.doi.org/10.56028/aetr.11.1.643.2024.

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In large language models (LLMs) field, the rapid advancements have significantly improved text generation, which has blured the distinction between AI-generated and human-written texts. These developments have sparked concerns about potential risks, such as disseminating fake information or engaging in academic cheating. As the responsible use of LLMs becomes imperative, the detection of AI-generated content has become a crucial task. Most existing surveys on AI-generated text (AIGT) Detection have analysed the detection approaches from a computational perspective, with less attention to linguistic aspects. This survey seeks to provide a fresh perspective to drive progress in the area of LLM-generated text detection. Futhermore, in order to make the assessment more explainable, we emphasize the great importence of leveraging specific parameters or metrics to linguistically evaluate the candidate text.

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A, Nykonenko. "How Text Transformations Affect AI Detection." Artificial Intelligence 29, AI.2024.29(4) (December 30, 2024): 233–41. https://doi.org/10.15407/jai2024.04.233.

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This study addresses the critical issue of AI writing detection, which currently plays a key role in deterring technology misuse and proposes a foundation for the controllable and conscious use of AI. The ability to differentiate between human-written and AI-generated text is crucial for the practical application of any policies or guidelines. Current detection tools are unable to interpret their decisions in a way that is understandable to humans or provide any human-readable evidence or proof for their decisions. We assume that there should be a traceable footprint in LLM-generated texts that is invisible to the human eye but can be detected by AI detection tools-referred to as the AI footprint. Understanding its nature will help bring more light into the guiding principles lying at the core of AI detection technology and help build more trust in the technology in general. The main goal of this paper is to examine the AI footprint in text data generated by large language models (LLMs). To achieve this, we propose a new method for text transformation that should measurably decrease the AI footprint in the text data, impacting AI writing scores. We applied a set of stage-by-stage text transformations focused on decreasing meaningfulness by masking or removing words. Using a set of AI detectors, we measured the AI writing score as a proxy metric for assessing the impact of the proposed method. The results demonstrate a significant correlation between the severity of changes and the resulting impact on AI writing scores, highlighting the need for developing more reliable AI writing identification methods that are immune to attempts to hide the AI footprint through subtle changes

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Singh, Dr Viomesh, Bhavesh Agone, Aryan More, Aryan Mengawade, Atharva Deshmukh, and Atharva Badgujar. "SAVANA- A Robust Framework for Deepfake Video Detection and Hybrid Double Paraphrasing with Probabilistic Analysis Approach for AI Text Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (November 30, 2024): 2074–83. http://dx.doi.org/10.22214/ijraset.2024.65526.

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Abstract: As the generative AI has advanced with a great speed, the need to detect AI-generated content, including text and deepfake media, also increased. This research work proposes a hybrid detection method that includes double paraphrasing-based consistency checks, coupled with probabilistic content analysis through natural language processing and machine learning algorithms for text and advanced deepfake detection techniques for media. Our system hybridizes the double paraphrasing framework of SAVANA with probabilistic analysis toward high accuracy on AI-text detection in forms such as DOCX or PDF from diverse domains- academic text, business text, reviews, and media. Specifically, for detecting visual artifact and spatiotemporal inconsistencies attributed to deepfakes within media applications, we'll be exploiting BlazeFace, EfficientNetB4 for extracting features while classifying and detecting respective deepfakes. Experimental results indicate that the hybrid model achieves up to 95% accuracy for AI-generated text detection and up to 96% accuracy for deepfake detection with the traditional models and the standalone SAVANA-based methods. This approach therefore positions our framework as an adaptive and reliable tool to detect AI-generated content within various contexts, thereby enriching content integrity in digital environments.

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Vismay Vora, Et al. "A Multimodal Approach for Detecting AI Generated Content using BERT and CNN." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (October 30, 2023): 691–701. http://dx.doi.org/10.17762/ijritcc.v11i9.8861.

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With the advent of Generative AI technologies like LLMs and image generators, there will be an unprecedented rise in synthetic information which requires detection. While deepfake content can be identified by considering biological cues, this article proposes a technique for the detection of AI generated text using vocabulary, syntactic, semantic and stylistic features of the input data and detecting AI generated images through the use of a CNN model. The performance of these models is also evaluated and benchmarked with other comparative models. The ML Olympiad Competition dataset from Kaggle is used in a BERT Model for text detection and the CNN model is trained on the CIFAKE dataset to detect AI generated images. It can be concluded that in the upcoming era, AI generated content will be omnipresent and no single model will truly be able to detect all AI generated content especially when these technologies are getting better.

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Subramaniam, Raghav. "Identifying Text Classification Failures in Multilingual AI-Generated Content." International Journal of Artificial Intelligence & Applications 14, no. 5 (September 28, 2023): 57–63. http://dx.doi.org/10.5121/ijaia.2023.14505.

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With the rising popularity of generative AI tools, the nature of apparent classification failures by AI content detection softwares, especially between different languages, must be further observed. This paper aims to do this through testing OpenAI’s “AI Text Classifier” on a set of human and AI-generated texts inEnglish, German, Arabic, Hindi, Chinese, and Swahili. Given the unreliability of existing tools for detection of AIgenerated text, it is notable that specific types of classification failures often persist in slightly different ways when various languages are observed: misclassification of human-written content as “AI-generated” and vice versa may occur more frequently in specific language content than others. Our findings indicate that false negative labelings are more likely to occur in English, whereas false positives are more likely to occur in Hindi and Arabic. There was an observed tendency for other languages to not be confidently labeled at all.

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Sushma D S, Pooja C N, Varsha H S, Yasir Hussain, and P Yashash. "Detection and Classification of ChatGPT Generated Contents Using Deep Transformer Models." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (May 23, 2024): 1404–7. http://dx.doi.org/10.47392/irjaeh.2024.0193.

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AI advancements, particularly in neural networks, have brought about groundbreaking tools like text generators and chatbots. While these technologies offer tremendous benefits, they also pose serious risks such as privacy breaches, spread of misinformation, and challenges to academic integrity. Previous efforts to distinguish between human and AI-generated text have been limited, especially with models like ChatGPT. To tackle this, we created a dataset containing both human and ChatGPT-generated text, using it to train and test various machine and deep learning models. Your results, particularly the high F1-score and accuracy achieved by the RoBERTa-based custom deep learning model and Distil BERT, indicate promising progress in this area. By establishing a robust baseline for detecting and classifying AI-generated content, your work contributes significantly to mitigating potential misuse of AI-powered text generation tools.

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Alshammari, Hamed, and Khaled Elleithy. "Toward Robust Arabic AI-Generated Text Detection: Tackling Diacritics Challenges." Information 15, no. 7 (July 19, 2024): 419. http://dx.doi.org/10.3390/info15070419.

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Current AI detection systems often struggle to distinguish between Arabic human-written text (HWT) and AI-generated text (AIGT) due to the small marks present above and below the Arabic text called diacritics. This study introduces robust Arabic text detection models using Transformer-based pre-trained models, specifically AraELECTRA, AraBERT, XLM-R, and mBERT. Our primary goal is to detect AIGTs in essays and overcome the challenges posed by the diacritics that usually appear in Arabic religious texts. We created several novel datasets with diacritized and non-diacritized texts comprising up to 9666 HWT and AIGT training examples. We aimed to assess the robustness and effectiveness of the detection models on out-of-domain (OOD) datasets to assess their generalizability. Our detection models trained on diacritized examples achieved up to 98.4% accuracy compared to GPTZero’s 62.7% on the AIRABIC benchmark dataset. Our experiments reveal that, while including diacritics in training enhances the recognition of the diacritized HWTs, duplicating examples with and without diacritics is inefficient despite the high accuracy achieved. Applying a dediacritization filter during evaluation significantly improved model performance, achieving optimal performance compared to both GPTZero and the detection models trained on diacritized examples but evaluated without dediacritization. Although our focus was on Arabic due to its writing challenges, our detector architecture is adaptable to any language.

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Jeremie Busio Legaspi, Roan Joyce Ohoy Licuben, Emmanuel Alegado Legaspi, and Joven Aguinaldo Tolentino. "Comparing ai detectors: evaluating performance and efficiency." International Journal of Science and Research Archive 12, no. 2 (July 30, 2024): 833–38. http://dx.doi.org/10.30574/ijsra.2024.12.2.1276.

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The widespread utilization of AI tools such as ChatGPT has become increasingly prevalent among learners, posing a threat to academic integrity. This study seeks to evaluate capability and efficiency of AI detection tools in distinguishing between human-authored and AI-generated works. Three-paragraph works on “AutoCAD and Architecture” were generated through ChatGPT, and three human-written works were subjected to evaluation. AI detection tools such as GPTZero, Copyleaks and Writer AI were used to evaluate these paragraphs. Parameters such as “Human/Human Text/Human Generated Text” and “AI/AI Content Detected” were used to evaluate the performance of the three AI detection tools in evaluating outputs. Findings indicate that GPT Zero and Copyleaks have higher reliability in determining human-authored work and AI generated work while Writer AI showed a notable content classification of “Human Generated Content” on all tested outputs showing less sensitivity on determining human-authored work and AI generated work. Findings indicate that the use of Artificial Intelligence as an AI detection tool should be accompanied with thorough validation and cross-referencing of results.

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Kim, Min-Gyu, and Heather Desaire. "Detecting the Use of ChatGPT in University Newspapers by Analyzing Stylistic Differences with Machine Learning." Information 15, no. 6 (May 25, 2024): 307. http://dx.doi.org/10.3390/info15060307.

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Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to study the presence of generative AI in newspaper articles written by collegiate journalists. To achieve this objective, an accurate AI detection model is needed. Herein, we analyzed university newspaper articles from different universities to determine whether ChatGPT was used to write or edit the news articles. We developed a detection model using classical machine learning and used the model to detect AI usage in the news articles. The detection model showcased a 93% accuracy in the training data and had a similar performance in the test set, demonstrating effectiveness in AI detection above existing state-of-the-art detection tools. Finally, the model was applied to the task of searching for generative AI usage in 2023, and we found that ChatGPT was not used to revise articles to any appreciable measure to write university news articles at the schools we studied.

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Dissertations / Theses on the topic "AI Generated Text Detection"

Pilana, Liyanage Vijini. "Detection of automatically generated academic Content." Electronic Thesis or Diss., Paris 13, 2024. http://www.theses.fr/2024PA131014.

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Dans cette thèse, nous avons concentré notre intérêt sur l'identification de technologies/méthodologies de détection de contenus académiques générés artificiellement. Les principales contributions de cette thèse sont triples. Tout d'abord, nous avons construit plusieurs corpus composés de textes académiques générés automatiquement. Dans cette tâche, nous avons utilisé plusieurs derniers modèles NLG pour la tâche de génération. Ces corpus contiennent des contenus entièrement générés ainsi que des contenus composés de manière hybride (avec intervention humaine). Ensuite, nous avons utilisé plusieurs modèles statistiques et d'apprentissage profond pour la détection des contenus générés à partir du contenu original (écrit par l'homme). Dans ce scénario, nous avons considéré la détection comme une tâche de classification binaire. Ainsi, plusieurs modèles de classification SOTA ont été utilisés. Les modèles ont été améliorés ou modifiés à l'aide de techniques d'assemblage pour obtenir une plus grande précision de détection. De plus, nous avons utilisé plusieurs outils de détection les plus récents pour identifier leur capacité à distinguer le texte généré automatiquement. Enfin, les corpus générés ont été testés par rapport aux bases de connaissances afin de trouver d'éventuelles inadéquations susceptibles d'aider à améliorer la tâche de détection. Les résultats de cette thèse soulignent l'importance d'imiter le comportement humain en tirant parti des modèles de génération ainsi que d'utiliser des corpus réalistes et stimulants dans les recherches futures visant à détecter des textes générés artificiellement. Enfin, nous souhaitons souligner le fait que, quelle que soit l'avancée de la technologie, il est toujours crucial de se concentrer sur l'aspect éthique de son utilisation
In this thesis, we have focused our interest on identifying technologies /methodologies in detecting artificially generated academic content. The principal contributions of this thesis are threefold. First, we built several corpora that are composed of machine generated academic text. In this task we utilized several latest NLG models for the generation task. These corpora contain contents that are fully generated as well as contents that are composed in a hybrid manner (with human intervention). Then, we employed several statistical as well as deep learning models for the detection of generated contents from original (human written) content. In this scenario, we considered detection as a binary classification task. Thus several SOTA classification models were employed. The models were improved or modified using ensembling techniques to gain higher accuracies in detection. Moreover, we made use of several latest detection tools to identify their capability in distinguishing machine generated text. Finally, the generated corpora were tested against knowledge bases to find any mismatches that could help to improve the detection task. The results of this thesis underline the importance of mimicking human behavior in leveraging the generation models as well of using realistic and challenging corpora in future research aimed at detecting artificially generated text. Finally, we would like to highlight the fact that no matter how advanced the technology is, it is always crucial to concentrate on the ethical aspect of making use of such technology

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Kurasinski, Lukas. "Machine Learning explainability in text classification for Fake News detection." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20058.

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Fake news detection gained an interest in recent years. This made researchers try to findmodels that can classify text in the direction of fake news detection. While new modelsare developed, researchers mostly focus on the accuracy of a model. There is little researchdone in the subject of explainability of Neural Network (NN) models constructed for textclassification and fake news detection. When trying to add a level of explainability to aNeural Network model, allot of different aspects have to be taken under consideration.Text length, pre-processing, and complexity play an important role in achieving successfully classification. Model’s architecture has to be taken under consideration as well. Allthese aspects are analyzed in this thesis. In this work, an analysis of attention weightsis performed to give an insight into NN reasoning about texts. Visualizations are usedto show how 2 models, Bidirectional Long-Short term memory Convolution Neural Network (BIDir-LSTM-CNN), and Bidirectional Encoder Representations from Transformers(BERT), distribute their attentions while training and classifying texts. In addition, statistical data is gathered to deepen the analysis. After the analysis, it is concluded thatexplainability can positively influence the decisions made while constructing a NN modelfor text classification and fake news detection. Although explainability is useful, it is nota definitive answer to the problem. Architects should test, and experiment with differentsolutions, to be successful in effective model construction.

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Nguyen, Minh Tien. "Détection de textes générés automatiquement." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM025/document.

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Le texte généré automatiquement a été utilisé dans de nombreuses occasions à des buts différents. Il peut simplement passer des commentaires générés dans une discussion en ligne à une tâche beaucoup plus malveillante, comme manipuler des informations bibliographiques. Ainsi, cette thèse introduit d'abord différentes méthodes pour générer des textes libres ayant trait à un certain sujet et comment ces textes peuvent être utilisés. Par conséquent, nous essayons d'aborder plusieurs questions de recherche. La première question est comment et quelle est la meilleure méthode pour détecter un document entièrement généré.Ensuite, nous irons un peu plus loin et montrer la possibilité de détecter quelques phrases ou un petit paragraphe de texte généré automatiquement en proposant une nouvelle méthode pour calculer la similarité des phrases en utilisant leur structure grammaticale. La dernière question est comment détecter un document généré automatiquement sans aucun échantillon, ceci est utilisé pour illustrer le cas d'un nouveau générateur ou d'un générateur dont il est impossible de collecter des échantillons dessus.Cette thèse étudie également l'aspect industriel du développement. Un aperçu simple d'un flux de travail de publication d'un éditeur de premier plan est présenté. À partir de là, une analyse est effectuée afin de pouvoir intégrer au mieux notre méthode de détection dans le flux de production.En conclusion, cette thèse a fait la lumière sur de multiples questions de recherche importantes concernant la possibilité de détecter des textes générés automatiquement dans différents contextes. En plus de l'aspect de la recherche, des travaux d'ingénierie importants dans un environnement industriel réel sont également réalisés pour démontrer qu'il est important d'avoir une application réelle pour accompagner une recherche hypothétique
Automatically generated text has been used in numerous occasions with distinct intentions. It can simply go from generated comments in an online discussion to a much more mischievous task, such as manipulating bibliography information. So, this thesis first introduces different methods of generating free texts that resemble a certain topic and how those texts can be used. Therefore, we try to tackle with multiple research questions. The first question is how and what is the best method to detect a fully generated document.Then, we take it one step further to address the possibility of detecting a couple of sentences or a small paragraph of automatically generated text by proposing a new method to calculate sentences similarity using their grammatical structure. The last question is how to detect an automatically generated document without any samples, this is used to address the case of a new generator or a generator that it is impossible to collect samples from.This thesis also deals with the industrial aspect of development. A simple overview of a publishing workflow from a high-profile publisher is presented. From there, an analysis is carried out to be able to best incorporate our method of detection into the production workflow.In conclusion, this thesis has shed light on multiple important research questions about the possibility of detecting automatically generated texts in different setting. Besides the researching aspect, important engineering work in a real life industrial environment is also carried out to demonstrate that it is important to have real application along with hypothetical research

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Nahnsen, Thade, Ozlem Uzuner, and Boris Katz. "Lexical Chains and Sliding Locality Windows in Content-based Text Similarity Detection." 2005. http://hdl.handle.net/1721.1/30546.

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We present a system to determine content similarity of documents. More specifically, our goal is to identify book chapters that are translations of the same original chapter; this task requires identification of not only the different topics in the documents but also the particular flow of these topics. We experiment with different representations employing n-grams of lexical chains and test these representations on a corpus of approximately 1000 chapters gathered from books with multiple parallel translations. Our representations include the cosine similarity of attribute vectors of n-grams of lexical chains, the cosine similarity of tf*idf-weighted keywords, and the cosine similarity of unweighted lexical chains (unigrams of lexical chains) as well as multiplicative combinations of the similarity measures produced by these approaches. Our results identify fourgrams of unordered lexical chains as a particularly useful representation for text similarity evaluation.

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Books on the topic "AI Generated Text Detection"

Schuman, Corey. Specimens of AI Generated Text: Aphorism Edition. Independently Published, 2020.

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Book chapters on the topic "AI Generated Text Detection"

Zhang, Yuehan, Yongqiang Ma, Jiawei Liu, Xiaozhong Liu, Xiaofeng Wang, and Wei Lu. "Detection Vs. Anti-detection: Is Text Generated by AI Detectable?" In Wisdom, Well-Being, Win-Win, 209–22. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57850-2_16.

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Najee-Ullah, Ahmad, Luis Landeros, Yaroslav Balytskyi, and Sang-Yoon Chang. "Towards Detection of AI-Generated Texts and Misinformation." In Socio-Technical Aspects in Security, 194–205. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10183-0_10.

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Carré, Marion. "Post-Truth." In Edition Museum, 187–98. Bielefeld, Germany: transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839467107-017.

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In this paper Marion Carré explores the intricate relationships between art, AI, archives, and truth through delving into the creation and detection of fake news and forgeries. By me ans of AI-generated texts, the study examines the living nature of archives and the impact of crowdsourcing on truth perception. AI's dual role in facilitating forgery and aiding detection is explored, thus emphasizing the importance of critical thin king and education as safeguards against deception. Museum professionals play a crucial role in raising awareness about the challenges posed by digital manipulation.

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(Mary) Tai, Hsueh-Yung. "Applications of Big Data and Artificial Intelligence." In Digital Health Care in Taiwan, 207–17. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05160-9_11.

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AbstractThis chapter introduces the application of National Health Insurance (NHI) big data in creating digital claim review tools and artificial intelligence (AI) training to improve review efficacy. By analyzing big data in the NHI medical information system, the National Health Insurance Administration (NHIA) can detect abnormal or unusual claims and efficiently reduce medical waste. AI models are further generated with the NHI big data to identify duplicated medical images and monitor the quality of uploaded images and test results from medical institutions.The NHIA also seeks external resources to explore the possibilities of diverse AI applications. Its big data have been applied to create an AI-based COVID-19 detection platform used by medical centers. Within it, high-risk patients’ X-ray images can be detected automatically and then an alert message is sent to doctors, thus preventing nosocomial COVID-19 infections.Besides a convenient digital claims system, the NHIA also provides contracted institutions with useful reminders, references, and graphic functions with figures and/or tables to help the quality of their self-management.

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Zhang, Huidi, Junming Gong, and Wei Wu. "Artificial Intelligence for Text Generation: An Intellectual Property Perspective." In AI-generated Content, 266–79. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7587-7_23.

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Bokolo, Biodoumoye George, Praise Onyehanere, Ebikela Ogegbene-Ise, Itunu Olufemi, and Josiah Nii Armah Tettey. "Leveraging Machine Learning for Crime Intent Detection in Social Media Posts." In AI-generated Content, 224–36. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7587-7_19.

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Gragnaniello, Diego, Francesco Marra, and Luisa Verdoliva. "Detection of AI-Generated Synthetic Faces." In Handbook of Digital Face Manipulation and Detection, 191–212. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_9.

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AbstractIn recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning methods it is now possible to generate visual data with a high level of realism. This is especially true for human faces. Advanced deep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic faces from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in the literature for the detection of synthetic faces. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.

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Cozzolino, Davide, Giovanni Poggi, Matthias Nießner, and Luisa Verdoliva. "Zero-Shot Detection of AI-Generated Images." In Lecture Notes in Computer Science, 54–72. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72649-1_4.

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Pham, Van Khien, and GueeSang Lee. "Robust Text Detection in Natural Scene Images." In AI 2016: Advances in Artificial Intelligence, 720–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50127-7_66.

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Liu, Chien-Liang, and Hung-Yu Kao. "CopyCAT: Masking Strategy Conscious Augmented Text for Machine Generated Text Detection." In Advances in Knowledge Discovery and Data Mining, 367–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33374-3_29.

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Conference papers on the topic "AI Generated Text Detection"

Chandana, Induri, Oruganti Mariya Reshma, Nerella Geetha Sree, Bommu Jagadeesh Reddy, and Syed Shareefunnisa. "Detecting AI Generated Text." In 2024 2nd World Conference on Communication & Computing (WCONF), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692028.

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Gupta, Shreya, and Deepa Gupta. "Detection and Classification of AI-Generated Text." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724991.

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Javaji, Prashanth, Pulaparthi Satya Sreeya, and Sudha Rajesh. "Detection of AI Generated Text With BERT Model." In 2024 2nd World Conference on Communication & Computing (WCONF), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692072.

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Kalra, Mehar Prateek, Ansh Mathur, and C. Patvardhan. "Detection of AI-generated Text: An Experimental Study." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), 552–57. IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10731116.

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Kuznetsov, Kristian, Eduard Tulchinskii, Laida Kushnareva, German Magai, Serguei Barannikov, Sergey Nikolenko, and Irina Piontkovskaya. "Robust AI-Generated Text Detection by Restricted Embeddings." In Findings of the Association for Computational Linguistics: EMNLP 2024, 17036–55. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.992.

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Gakpetor, Joshua Mawudem, Martin Doe, Michael Yeboah-Sarpong Damoah, Dominic Dalyngton Damoah, John Kingsley Arthur, and Michael Tetteh Asare. "AI-Generated and Human-Written Text Detection Using DistilBERT." In 2024 IEEE SmartBlock4Africa, 1–7. IEEE, 2024. https://doi.org/10.1109/smartblock4africa61928.2024.10779494.

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Vacariu, Andrei-Nicolae, Marian Bucos, Marius Otesteanu, and Bogdan Dragulescu. "Automated Detection of AI-Generated Text Using LLM Embedding-Driven ML Models." In 2024 International Symposium on Electronics and Telecommunications (ISETC), 1–4. IEEE, 2024. https://doi.org/10.1109/isetc63109.2024.10797258.

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Aggarwal, Kush, Sahib Singh, Parul, Vipin Pal, and Satyendra Singh Yadav. "A Framework for Enhancing Accuracy in AI Generated Text Detection Using Ensemble Modelling." In 2024 IEEE Region 10 Symposium (TENSYMP), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752173.

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Kavathekar, Ishan, Anku Rani, Ashmit Chamoli, Ponnurangam Kumaraguru, Amit P. Sheth, and Amitava Das. "Counter Turing Test (CT2): Investigating AI-Generated Text Detection for Hindi - Ranking LLMs based on Hindi AI Detectability Index (ADI_hi)." In Findings of the Association for Computational Linguistics: EMNLP 2024, 4902–26. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.282.

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Huang, Guanhua, Yuchen Zhang, Zhe Li, Yongjian You, Mingze Wang, and Zhouwang Yang. "Are AI-Generated Text Detectors Robust to Adversarial Perturbations?" In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 6005–24. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.327.

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