  In this letter, we explored generative image steganography based on
autoregressive models. We proposed Pixel-Stega, which implements pixel-level
information hiding with autoregressive models and arithmetic coding algorithm.
Firstly, one of the autoregressive models, PixelCNN++, is utilized to produce
explicit conditional probability distribution of each pixel. Secondly, secret
messages are encoded to the selection of pixels through steganographic sampling
(stegosampling) based on arithmetic coding. We carried out qualitative and
quantitative assessment on gray-scale and colour image datasets. Experimental
results show that Pixel-Stega is able to embed secret messages adaptively
according to the entropy of the pixels to achieve both high embedding capacity
(up to 4.3 bpp) and nearly perfect imperceptibility (about 50% detection
accuracy).
