Search Results for author:
Found 25 papers, 10 papers with code
Machine Learning Based Detection of Clickbait Posts in Social Media
In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge. com) comprising of over 21, 000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is.
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats.
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem.
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher
Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch.
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks
The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method.
Large-Scale Data-Driven Airline Market Influence Maximization
On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning
To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected.
Do Language Models Plagiarize?
Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs' plagiarism patterns vary based on their corpus similarity and homogeneity.
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective
Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO).
CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild
CRYPTEXT is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations.
NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online
In this paper, we introduce a benchmark test set containing human-written perturbations online for toxic speech detection models.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
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.
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack
LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications--e. g., healthcare and finance.
TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
We propose TopFormer to improve existing AA solutions by capturing more linguistic patterns in deepfake texts by including a Topological Data Analysis (TDA) layer in the Transformer-based model.
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark
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.
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.
Marrying Adapters and Mixup to Efficiently Enhance the Adversarial Robustness of Pre-Trained Language Models for Text Classification
Existing works show that augmenting training data of neural networks using both clean and adversarial examples can enhance their generalizability under adversarial attacks.
ALISON: Fast and Effective Stylometric Authorship Obfuscation
AO is the corresponding adversarial task, aiming to modify a text in such a way that its semantics are preserved, yet an AA model cannot correctly infer its authorship.
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning
Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of them at once.
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models
Traditional adversarial evaluation is often done \textit{only after} fine-tuning the models and ignoring the training data.
Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands
The COVID-19 pandemic has created threats to global health control.
Authorship Attribution for Neural Text Generation
In recent years, the task of generating realistic short and long texts have made tremendous advancements.
