Learning Iterative Neural Optimizers for Image Steganography
This addresses the challenge of reliably hiding secret information in images for secure communication, representing a significant improvement over existing methods.
The paper tackled the problem of image steganography by proposing an iterative neural network that optimizes while staying close to the natural image manifold, resulting in zero error up to 3 bits per pixel and faster performance compared to classical methods.
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.