# Towards AI-Generated Essay Classification Using Numerical Text Representation
## 2 Related Works
The proposed model, with no character count constraints, achieved an accuracy of 89.09%, outperforming the OpenAI classifier. ZeroGPT, using a perplexity score, achieved an accuracy of 87.50% [7]. The authors noted that their model had similar false positive and false negative rates compared to ZeroGPT. However, the model's limitation was the use of basic techniques, and different approaches for numerical text representation were not compared. The authors plan to explore various preprocessing techniques in future work. The need for effective anomaly detection in textual data has prompted extensive comparisons between various text representation methods, particularly focusing on the effectiveness of frequency-based versus prediction-based models [8]. The authors have utilized pre-trained models, fastText and Word2 vec, using Skip-gram and Continuous Bag of Words (CBoW) approaches to classify legal emails and phishing attacks. They used three datasets, embedding words using frequency, TF-IDF, Word2 vec (CBoW and Skip-gram), and fastText (CBoW and Skip-gram) methods. Various vector lengths were tested, and classification was performed using random forests, decision trees, XGBoost, logistic regression, and support vector machines. The highest accuracy for the first dataset was 99.50% using fastText (CBoW), random forests, and a vector length of 100. For the second dataset, the highest accuracy was 99.39% using TF-IDF, random forests, and a vector length of 200. The highest accuracy of the third dataset was 99.18% using word frequency, random forests, and a vector length of 150. In addition to the challenges posed by AI-generated text detection, recent advancements have highlighted the growing threat of adversarial attacks in the field of text classification, where subtle modifications to the text can deceive classifiers into making incorrect predictions. In this context, the robustness of text representation techniques becomes critical. For instance, [PERSON] and [PERSON] [9] proposed a defense against textual backdoor attacks by using class differences for detection. Their method compares a detection model trained on secure, partial data with a target model trained on the entire dataset, demonstrating the importance of high-quality text representations to accurately identifying manipulated samples. The study reported detection rates above 79% on the MR and IMDB datasets, demonstrating how tailored detection models can mitigate backdoor attacks without needing full access to the training dataset. Furthermore, [PERSON] and [PERSON] [10] explored defenses against adversarial examples in text classification systems, which involve modifying important words in a text to cause misclassification. Their proposed method leverages text modification by replacing key words with semantically similar alternatives. This approach exploits the fact that adversarial examples are more sensitive to such modifications than original sentences, enabling the model to distinguish between the two. Their experiments on datasets like IMDB and MR showed that this technique can achieve an adversarial detection rate of over 71%, underscoring the importance of advanced text representations in improving model robustness against adversarial manipulations.