Cheng-Ta Huang

The menace of fake news has gained attention through the trend of the convenience of information-sharing in the cyber world. The information-sharing lifestyle has penetrated people's cyber routines. This study adopts a victimology perspective to explore the characteristics of the audience that is most likely to be influenced by fake news in social media through secondary data quantitative research. The results show that demographic characteristics, gender, and education level affect the likelihood of receiving fake news. When people use more social media platforms and have greater engagement, such as more sharing, reposting, and replying on social media platforms, there is a greater chance of receiving fake news.

This paper presents a novel reversible data hiding (RDH) technique based on pixel value ordering (PVO) and median preserving (MP) for improving image quality. In the proposed method, an adaptive block-wise embedding mechanism is applied. In PVO-based RDH, embedding data into a smooth block is always leading to a better image quality. For each block, the complexity is first calculated using surrounding blocks and then set a threshold in order to measure whether this block is a smooth or a complex block. The smooth blocks are embedded with secret data, and the complex blocks are skipped based on the threshold value. Therefore, this mechanism better exploits smooth blocks for data embedding, resulting in improved image quality. Experimental results demonstrate that the proposed method achieves a higher PSNR value than the previous approach.

The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.