Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach
This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. Rapid advancements in natural language processing models have led to the generation of text closely resembling human language, making it increasingly...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10971184/ |
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| _version_ | 1849311242177478656 |
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| author | John Blake Abu Saleh Musa Miah Krzysztof Kredens Jungpil Shin |
| author_facet | John Blake Abu Saleh Musa Miah Krzysztof Kredens Jungpil Shin |
| author_sort | John Blake |
| collection | DOAJ |
| description | This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. Rapid advancements in natural language processing models have led to the generation of text closely resembling human language, making it increasingly difficult to differentiate between human and AI-generated content. However, misuse of such texts presents a serious and imminent threat to the quality of academic publishing. This underscores the urgent need for robust detection mechanisms to ensure information quality, maintain trust, and preserve the integrity of research publications. Our proposed model outperformed existing algorithms for accuracy with less computational complexity. The proposed model is a feature-based hybrid deep learning network that leverages part-of-speech tagging and integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with Attention modules. The initial module extracts local contextual features using convolutional layers, followed by Bi-LSTM layers that capture long-term dependencies from past and future sequences. An attention mechanism highlights critical sequence components, enhancing the model’s focus on relevant data. The outputs from the attention and initial modules are concatenated through a residual connection, ensuring comprehensive feature representation. This combination is then fed into dense layers for final classification, effectively balancing feature richness and computational efficiency. The proposed model was evaluated on two benchmark datasets, achieving 85.00% and 88.00% accuracy, respectively. |
| format | Article |
| id | doaj-art-37ebe036f23e48828e2b48161097f7fd |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-37ebe036f23e48828e2b48161097f7fd2025-08-20T03:53:28ZengIEEEIEEE Access2169-35362025-01-0113715637157610.1109/ACCESS.2025.356275010971184Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based ApproachJohn Blake0https://orcid.org/0000-0002-3150-4995Abu Saleh Musa Miah1https://orcid.org/0000-0002-1238-0464Krzysztof Kredens2Jungpil Shin3https://orcid.org/0000-0002-7476-2468School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanSchool of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanAston Institute for Forensic Linguistics, Aston University, Birmingham, U.K.School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanThis paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. Rapid advancements in natural language processing models have led to the generation of text closely resembling human language, making it increasingly difficult to differentiate between human and AI-generated content. However, misuse of such texts presents a serious and imminent threat to the quality of academic publishing. This underscores the urgent need for robust detection mechanisms to ensure information quality, maintain trust, and preserve the integrity of research publications. Our proposed model outperformed existing algorithms for accuracy with less computational complexity. The proposed model is a feature-based hybrid deep learning network that leverages part-of-speech tagging and integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with Attention modules. The initial module extracts local contextual features using convolutional layers, followed by Bi-LSTM layers that capture long-term dependencies from past and future sequences. An attention mechanism highlights critical sequence components, enhancing the model’s focus on relevant data. The outputs from the attention and initial modules are concatenated through a residual connection, ensuring comprehensive feature representation. This combination is then fed into dense layers for final classification, effectively balancing feature richness and computational efficiency. The proposed model was evaluated on two benchmark datasets, achieving 85.00% and 88.00% accuracy, respectively.https://ieeexplore.ieee.org/document/10971184/AI-generated text detectionauthorship analysisauthorship verificationmachine-generated text detection |
| spellingShingle | John Blake Abu Saleh Musa Miah Krzysztof Kredens Jungpil Shin Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach IEEE Access AI-generated text detection authorship analysis authorship verification machine-generated text detection |
| title | Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach |
| title_full | Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach |
| title_fullStr | Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach |
| title_full_unstemmed | Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach |
| title_short | Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach |
| title_sort | detection of ai generated texts a bi lstm and attention based approach |
| topic | AI-generated text detection authorship analysis authorship verification machine-generated text detection |
| url | https://ieeexplore.ieee.org/document/10971184/ |
| work_keys_str_mv | AT johnblake detectionofaigeneratedtextsabilstmandattentionbasedapproach AT abusalehmusamiah detectionofaigeneratedtextsabilstmandattentionbasedapproach AT krzysztofkredens detectionofaigeneratedtextsabilstmandattentionbasedapproach AT jungpilshin detectionofaigeneratedtextsabilstmandattentionbasedapproach |