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).
翻译:在这封信中,我们探索了基于自动递减模型的基因化图像色谱学。 我们提议了像素- Stega, 以自动递减模型和算法编码算法来实施像素级信息。 首先, 自动递减模型之一, 像素CNN++, 用来生成每个像素的明显有条件的概率分布。 其次, 秘密信息被编码为通过基于算术编码的像素抽样( stegosamping) 来选择像素。 我们对灰度和彩色图像数据集进行了定性和定量评估。 实验结果显示, 像素- 斯特加能够根据像素的昆虫来适应地嵌入秘密信息, 以实现高嵌入能力( 至4.3 bpp) 和近乎完美的不易感知性( 约50% 检测准确性) 。