Robust Multi-bit Natural Language Watermarking through Invariant Features

Recent years have witnessed a proliferation of valuable original natural language contents found in subscription-based media outlets, web novel platforms, and outputs of large language models. However, these contents are susceptible to illegal piracy and potential misuse without proper security measures. This calls for a secure watermarking system to guarantee copyright protection through leakage tracing or ownership identification. To effectively combat piracy and protect copyrights, a multi-bit watermarking framework should be able to embed adequate bits of information and extract the watermarks in a robust manner despite possible corruption. In this work, we explore ways to advance both payload and robustness by following a well-known proposition from image watermarking and identify features in natural language that are invariant to minor corruption. Through a systematic analysis of the possible sources of errors, we further propose a corruption-resistant infill model. Our full method improves upon the previous work on robustness by +16.8% point on average on four datasets, three corruption types, and two corruption ratios. Code available at https://github.com/bangawayoo/nlp-watermarking.Comment: ACL 2023 lon

A Mental Workload Estimation Model for Visualization Using EEG

Various visualization design guides have been proposed and evaluated through quantitative methods that compare the response accuracy and time for completing visualization tasks. However, accuracy and time do not always represent the mental workload. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The EEG as biosignal is one of the indicators frequently utilized to measure mental workload. Nevertheless, many studies have not applied the EEG for mental workload measurement in the visualization evaluation. In this work, we study the EEG to measure mental workload for visualization evaluation. We examine whether there is a difference in mental workload for the visualization designs suggested by the previously proposed visualization design guides. Besides, we propose a mental workload estimation model using EEG data specialized for each individual to evaluate visualization designs