Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder-based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes.

翻译：图像隐写术是通过不可察觉的修改将秘密信息隐藏于图像中的过程。近期研究将该任务形式化为经典约束优化问题。本文认为图像隐写术本质上是在自然图像（难以捉摸的）流形上执行的，并提出一种为执行优化步骤而训练的迭代神经网络。与L-BFGS或投影梯度下降等经典优化方法不同，我们在整个优化过程中训练神经网络使其始终贴近自然图像流形。实验表明，我们提出的学习型神经优化比经典优化方法更快、更可靠。与以往基于编码器-解码器的最先进隐写方法相比，该方法将恢复错误率降低多个数量级，并在无需纠错码的情况下实现高达3比特每像素（bpp）的零错误率。