# The New Paradigm of Deepfake Detection at the Text Level
## 5 Discussion  
Models based on LLM focus on identifying AI-specific patterns but do not identify each author's specific style. Previous studies have detected texts generated by AI models, but cannot identify whether a text is associated with a particular author. Therefore, the authors focused on individual stylistic features identified at the text level in this paper. The CDFTM model analyzed four combinations of unique features at the text level. The four combinations were represented by the _AW_, _AC_, _UR_, and _CL_ (sentiment analysis expressed in the text). The dataset of the CDFTM model consisted of 1300 records, with 650 representing deepfakes generated by AI and 650 being texts generated by humans. Tests showed that only three provide uniqueness at the text level: _AW_, _UR_, and _CL_. The model's architecture was established through a comparative analysis of eight algorithms. The LightGBM algorithm was associated with the highest accuracy. After the CDFTM model determines whether a text is \"AI\" or \"Human\", if the result is \"Human\", the text is further evaluated to determine if it is associated with a specific author. The paper highlights the context of the unethical practice of using AI in content generation and attributing the text to a certain person. By introducing the ADFTM, the paper offers a new perspective that identifies whether a text is associated with a specific author, thus complementing existing research. To achieve this goal, several configurations of anomaly-detection algorithms addressing the unique features of a specific author's texts were analyzed. For this, nine text-level features were analyzed within the ADFTM. Since the results did not provide very high accuracy, the tests were reduced to three features, namely _AW_, _UR_, and _CL_, which were also used for training the CDFTM model. These features significantly contributed to establishing this uniqueness. The dataset of the ADFTM consisted of 400 records from one author, and anomaly detection was performed on another 50 texts from a second author. Two algorithms were analyzed in the anomaly detection: Randomized PCA and K-Means Clustering. The results showed that Randomized PCA provided results superior to those of K-Means Clustering, and with the same accuracy, regardless of whether nine features, three features, or only one uniqueness feature were used as input, leading to the idea that the three features _AW_, _UR_, and _CL_ are the ones that determine the uniqueness of the text. The CDFTM model classifies a text as human or AI. Experimental results achieved an accuracy of 98% in correctly identifying texts. This performance shows that text characteristics in pattern establishment vary depending on the need. Thus, the CDFTM model used characteristics such as sentence length, vocabulary diversity, and emotional neutrality level. Figure 18: _UR_ for authors 1 and 2, using RandomizedPCA (3 entries). At the same time, ADFTM required a much larger number of unique text characteristics, and was based on the analyses of the anomalies of these characteristics. Tests showed that the ADFTM achieves an accuracy of 88.9%. This value indicates that the model can identify texts from a particular author. However, it also shows that further improvements are needed. The two models, CDFTM and ADFTM, achieved high accuracy values, and were able to identify whether a text is a deepfake. Notably, these models use limited datasets to build patterns at the text level for an author's profile. Adding much larger datasets will undoubtedly improve the metrics of both algorithms. Second, the evolution of an author's writing style can affect detection accuracy over time. For this reason, constant improvement of both models is necessary.