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更改,在图像中隐藏秘密信息的过程。最近的研究将这个任务表述为一个经典的约束优化问题。本文认为,图像隐写术本质上是在自然图像的(难以捉摸的)流形上执行的,并且提出了一种经过训练的迭代神经网络来执行优化步骤。与像L-BFGS或投影梯度下降这样的经典优化方法不同,我们训练神经网络在整个优化过程中也保持接近自然图像的流形。我们证明,我们所学习的神经优化方法比经典的优化方法更快速、更可靠。与前面的最先进的编码器-解码器式隐写术方法相比,它将恢复误差率降低了多个数量级,并在3比特/像素(bpp)之内实现零误差而无需纠错码