Search Results for author: Adaku Uchendu

Found 12 papers, 5 papers with code

Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks

no code implementations16 Nov 2021 Adaku Uchendu, Daniel Campoy, Christopher Menart, Alexandra Hildenbrandt

Bayesian Neural Networks (BNNs), unlike Traditional Neural Networks (TNNs) are robust and adept at handling adversarial attacks by incorporating randomness.

Adversarial Attack Bayesian Inference

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

no code implementations19 Oct 2022 Adaku Uchendu, Thai Le, Dongwon Lee

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO).

Attribute Authorship Attribution +1

Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

2 code implementations3 Apr 2023 Adaku Uchendu, Jooyoung Lee, Hua Shen, Thai Le, Ting-Hao 'Kenneth' Huang, Dongwon Lee

Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.

Face Swapping Human Detection +1

TopRoBERTa: Topology-Aware Authorship Attribution of Deepfake Texts

no code implementations22 Sep 2023 Adaku Uchendu, Thai Le, Dongwon Lee

We propose \textbf{TopRoBERTa} to improve existing AA solutions by capturing more linguistic patterns in deepfake texts by including a Topological Data Analysis (TDA) layer in the RoBERTa model.

Authorship Attribution Face Swapping +3

GPT-who: An Information Density-based Machine-Generated Text Detector

1 code implementation9 Oct 2023 Saranya Venkatraman, Adaku Uchendu, Dongwon Lee

We examine if this UID principle can help capture differences between Large Language Models (LLMs)-generated and human-generated texts.

Authorship Attribution

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

1 code implementation20 Oct 2023 Dominik Macko, Robert Moro, Adaku Uchendu, Jason Samuel Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova

There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings.

Benchmarking Text Detection

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

1 code implementation24 Oct 2023 Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee, Shaurya Rohatgi, Dongwon Lee

Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (. i. e, generating large-scale harmful and misleading content).

A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts

no code implementations14 Nov 2023 Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee

In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.

Authorship Attribution for Neural Text Generation

no code implementations EMNLP 2020 Adaku Uchendu, Thai Le, Kai Shu, Dongwon Lee

In recent years, the task of generating realistic short and long texts have made tremendous advancements.

Authorship Attribution Text Generation

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