no code implementations • 29 Jan 2024 • Fatma Shalabi, Huy H. Nguyen, Hichem Felouat, Ching-Chun Chang, Isao Echizen
Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods.
no code implementations • 16 Jan 2024 • Zhicheng Dou, Yuchen Guo, Ching-Chun Chang, Huy H. Nguyen, Isao Echizen
In this paper, we present a comprehensive analysis of the impact of prompts on the text generated by LLMs and highlight the potential lack of robustness in one of the current state-of-the-art GPT detectors.
no code implementations • 26 Feb 2022 • Ching-Chun Chang
Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management.
no code implementations • 5 Feb 2022 • Ching-Chun Chang, Xu Wang, Sisheng Chen, Hitoshi Kiya, Isao Echizen
The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data.
no code implementations • 7 Jan 2022 • Ching-Chun Chang
A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks.
no code implementations • 13 Jun 2021 • Ching-Chun Chang, Xu Wang, Sisheng Chen, Isao Echizen, Victor Sanchez, Chang-Tsun Li
Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility.