Topology-Aware Authorship Attribution: Detecting Deepfake Texts with Diverse Writing Styles

Core Concepts

TOPFORMER, a hybrid model that combines a Transformer-based backbone (RoBERTa) with Topological Data Analysis (TDA), can accurately attribute authorship of deepfake texts, even in the presence of imbalanced, multi-style datasets.

Abstract

The paper proposes TOPFORMER, a novel solution for accurately attributing the authorship of deepfake texts versus human-written texts. TOPFORMER combines a Transformer-based backbone (RoBERTa) with Topological Data Analysis (TDA) to capture both contextual representations (semantic and syntactic features) and the shape/structure of the data (linguistic structures).
The key highlights are:

Recent advances in Large Language Models (LLMs) have enabled the generation of high-quality deepfake texts that are non-trivial to distinguish from human-written texts.
The Authorship Attribution (AA) problem, which aims to not only detect if a text is a deepfake but also identify the specific LLM author, is more challenging than the Turing Test (TT) problem.
TOPFORMER outperforms state-of-the-art deepfake text attribution models on three realistic datasets (OpenLLMText, SynSciPass, Mixset) that reflect the current landscape of diverse writing styles and label imbalance.
TDA features extracted from the reshaped pooled output of the RoBERTa backbone complement the contextual representations, enabling TOPFORMER to capture both semantic/syntactic and structural linguistic patterns.
TOPFORMER performs well on datasets with multi-style labels, suggesting its robustness to heterogeneous data. It also performs comparably to the backbone model on more homogeneous datasets.

Stats

There are currently over 72K text generation models in the huggingface model repo.
The OpenLLMText dataset has 53K training, 10K validation, and 7.7K test samples across 5 labels (human, LLaMA, ChatGPT, PALM, GPT-2).
The SynSciPass dataset has 87K training, 10K validation, and 10K test samples across 12 labels (1 human, 11 deepfake text generators).
The Mixset dataset has 2.4K training, 340 validation, and 678 test samples across 8 labels (human, GPT-4, LLaMA, Dolly, ChatGLM, StableLM, ChatGPT-turbo, ChatGPT).

Quotes

None

Key Insights Distilled From

TOPFORMER

by Adaku Uchend... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2309.12934.pdf

Deeper Inquiries

How can TOPFORMER's performance be further improved, especially on more homogeneous datasets?

To further improve TOPFORMER's performance on more homogeneous datasets, several strategies can be implemented:

Fine-tuning on Specific Features: Instead of relying solely on the TDA features, TOPFORMER could be fine-tuned to focus on specific linguistic features that are more prevalent in homogeneous datasets. This targeted fine-tuning can help the model better distinguish between human-written and deepfake texts in such datasets.

Data Augmentation: Generating synthetic data points that mimic the characteristics of the homogeneous dataset can help TOPFORMER learn more effectively from the limited data available. By augmenting the dataset with variations of existing samples, the model can improve its performance on these datasets.

Ensemble Methods: Combining TOPFORMER with other models that excel in handling homogeneous datasets can lead to a more robust performance. Ensemble methods can leverage the strengths of different models to compensate for individual weaknesses.

Regularization Techniques: Implementing regularization techniques like dropout or L2 regularization can prevent overfitting on homogeneous datasets, ensuring that the model generalizes well to unseen data.

Hyperparameter Tuning: Optimizing hyperparameters specific to the characteristics of homogeneous datasets can significantly enhance TOPFORMER's performance. Parameters like learning rate, batch size, and optimizer choice can be fine-tuned for better results.

By incorporating these strategies, TOPFORMER can adapt more effectively to homogeneous datasets and improve its overall performance in authorship attribution tasks.

How can the TDA features extracted by TOPFORMER be interpreted to gain deeper insights into the linguistic patterns that distinguish human-written texts from deepfake texts?

The TDA features extracted by TOPFORMER offer a unique perspective on the linguistic patterns present in human-written and deepfake texts. These features can be interpreted to gain deeper insights into the distinctions between the two types of texts:

Structural Differences: TDA features capture the structural properties of textual data, such as the presence of loops, voids, and connected components. By analyzing these features, researchers can identify underlying structural differences between human-written and deepfake texts.

Semantic Complexity: TDA features can reveal the semantic complexity of texts by highlighting the persistence of certain linguistic patterns across different dimensions. Deeper persistence in specific features may indicate more intricate semantic structures in human-written texts compared to deepfakes.

Syntactic Variability: TDA features can also shed light on the syntactic variability between human and deepfake texts. Variations in the birth and death times of features across different dimensions can signify diverse syntactic patterns unique to each type of text.

Contextual Understanding: By examining the relationships between TDA features and the context of the text, researchers can infer how linguistic elements interact within the text. This contextual understanding can provide valuable insights into the nuances that differentiate human and deepfake writing styles.

Pattern Recognition: TDA features enable the identification of recurring linguistic patterns that are characteristic of human-written texts but absent in deepfakes. By recognizing these patterns, TOPFORMER can effectively attribute authorship based on the distinctive linguistic signatures present in the text.

Overall, interpreting TDA features extracted by TOPFORMER offers a deeper understanding of the linguistic nuances that distinguish human-written texts from deepfake texts, providing valuable insights for authorship attribution tasks.

What other applications beyond authorship attribution could benefit from the combination of Transformer-based models and Topological Data Analysis?

The combination of Transformer-based models and Topological Data Analysis (TDA) can be leveraged in various applications beyond authorship attribution:

Sentiment Analysis: By integrating TDA with Transformer models, sentiment analysis tasks can benefit from capturing the underlying structural and semantic features of text data. This combination can enhance the understanding of sentiment nuances and improve sentiment classification accuracy.

Anomaly Detection: The fusion of Transformer models with TDA can enhance anomaly detection in various domains such as cybersecurity, fraud detection, and healthcare. TDA can help identify unusual patterns or outliers in data, while Transformer models can provide contextual understanding for more accurate anomaly detection.

Text Generation: Incorporating TDA into Transformer-based text generation models can improve the generation of coherent and contextually relevant text. TDA features can guide the generation process by ensuring the structural integrity and coherence of the generated text.

Information Retrieval: The combination of Transformer models and TDA can enhance information retrieval systems by extracting and analyzing the topological features of textual data. This approach can improve the relevance and accuracy of search results by considering the structural relationships between documents.

Language Understanding: Transformer models augmented with TDA can advance language understanding tasks such as question-answering, summarization, and dialogue systems. TDA features can provide additional insights into the linguistic structures of text data, leading to more nuanced language processing capabilities.

By applying the synergy between Transformer-based models and TDA in these diverse applications, researchers and practitioners can unlock new possibilities for enhancing text analysis, understanding, and generation across various domains.