Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces $1.926\times$ more innocent and intelligible stegotext than any other method.

IBM Data and AI general manager Rob Thomas discusses AI being incorporated into the workforce. The labor market may be humming right now, but there may be a dark cloud looming ahead. Over the course of the next decade, up to 800 million jobs globally could disappear due to advances in artificial intelligence and robotics, according to research from the McKinsey Global Institute, a top consulting firm. An estimated one-third of the 2030 workforce in the U.S. may need to learn new skills and find work in new occupations. The changes won't hit the country equally.