Random at G en AI Detection Task 3: A Hybrid Approach to Cross-Domain Detection of Machine-Generated Text with Adversarial Attack Mitigation (2025)

Abstract: Machine-generated text (MGT) detection has gained critical importance in the era of large language models, especially for maintaining trust in multilingual and cross-domain applica- tions. This paper presents Task 3 Subtask B: Adversarial Cross-Domain MGT Detection for in the COLING 2025 DAIGenC Workshop. Task 3 emphasizes the complexity of detecting AI-generated text across eight domains, eleven generative models, and four decoding strate- gies, with an added challenge of adversarial manipulation. We propose a robust detection framework transformer embeddings utilizing Domain-Adversarial Neural Networks (DANN) to address domain variability and adversarial robustness. Our model demonstrates strong performance in identifying AI-generated text under adversarial conditions while highlighting condition scope of future improvement.

## introduction 2 introduction the advent of large-scale generative language models, such as gpt (generative pre-trained transformer) @xcite based systems, has revolutionized text generation by producing outputs that closely mimic human writing. these advancements have significantly impacted various fields, including content creation, education, and customer service, by providing highly coherent and contextually relevant text. however, this progress has also introduced new challenges, particularly in detecting mgt across diverse domains and languages. the widespread use of these models has raised concerns in areas like academic integrity, misinformation, and malicious use of ai-generated content. while there are existing models result mention in raid 1 , still struggle with cross-domain issues and 1 https://gitlab.com/genai-content-detection/genaicontent-detection-coling-2025 adversarial attacks (manipulations of input data to fool machine learning models). to tackle these issues, the coling 2025 workshop on daigenc foot_0 @xcite ) "task 3: cross-domain machine-generated text detection" binary problem formulation as task 1 however the texts will come from 8 different domains, 11 generative models, and four decoding strategies. this challenge is divided into two sub-tasks: non-adversarial and adversarial. we solve one subtask, subtask b, which is adversarial cross-domain mgt detection. our contributions include a pipeline that integrates xlm-roberta @xcite embeddings for enhanced text representation, domain adaptation using domain-adversarial neural networks (dann) to minimize domain-specific biases and improve generalization across diverse text domains, and adversarial robustness through incorporating adversarial attack classification to detect and mitigate manipulative techniques. additionally, we focus on label prediction for improved model accuracy. experimental results demonstrate strong detection performance, particularly in the same domain and against zero-width space attacks. our findings from experimental results highlight the strengths of the proposed approach while identifying areas for improvement, such as achieving consistent performance across all domains and all attack types. these results emphasize the importance of balanced datasets, adaptive techniques, and comprehensive evaluation to advance the field of mgt detection. ## background over the last few years, numerous approaches have been proposed to tackle the task of ai-generated text detection. detecting machine-generated text is primarily formulated as a binary classification task @xcite @xcite , naively distinguishing between human-written and machine-generated text. in general, there are three main approaches: the supervised methods @xcite @xcite @xcite , the unsupervised ones, such as zero-shot methods @xcite @xcite @xcite , and adversarial measures on detection accuracy @xcite , especially within the education domain. for example, @xcite evaluates the robustness of detectors against character-level perturbations or misspelled words, focusing on french as a case study. @xcite train a generative model (dip-per) to paraphrase paragraphs to evade detection. although supervised approaches yield relatively better results, they are susceptible to overfitting @xcite . there are some techniques like feature-based, fusion, and ensemble methods neural based or finetuned @xcite @xcite @xcite . therefore, researchers combine statistics-based and deep learning-based techniques to gain adversarial robustness and high performance @xcite @xcite .some studies attempt to address the challenges of cross-domain detection and adversarial attacks @xcite . ## system overview we present our proposed adversarial cross-domain mgt detection which combines adaptability across diverse domains, attacks and generative models. ## experimental setup we utilize the dataset mentioned in section 4.1 further details of experimental setup and hyperparameters are presented in the appendix section a.1. our model was implemented using pytorch. ## results as mentioned in in summary, our detector demonstrates optimal performance in the wikipedia domain, with the greedy decoding strategy, the repetition penalty enabled, the zero-width space adversarial attacks, and text generated by the mptchat model. all details of results are mentioned in the raid cooling shared task leaderboard. ## conclusion this paper introduces a robust approach to adversarial cross-domain mgt detection, leveraging transformer embeddings and domain adaptation to address the challenges of domain variability and adversarial robustness. the proposed architecture, based on domain-adversarial neural networks, demonstrates strong performance, particularly in detecting machine-generated text from specific domains like wikipedia and against attacks like homoglyphs and zero-width space manipulations. but still fail to generalization. future work should focus on curating more balanced datasets, enhancing model adaptability to diverse attack types, and exploring lightweight architectures for real-time applications. these steps are crucial to advance the reliability and scalability of mgt detection systems in multilingual and cross-domain scenarios. wanjun zhong, duyu tang, zenan xu, ruize wang, nan duan, ming zhou, jiahai wang, and jian yin. 2020. neural deepfake detection with factual structure of text. arxiv preprint arxiv:2010.07475.