Image steganography is the art of concealing secret information in images in a way that is imperceptible to unauthorized parties. Recent advances show that is possible to use a fixed neural network (FNN) for secret embedding and extraction. Such fixed neural network steganography (FNNS) achieves high steganographic performance without training the networks, which could be more useful in real-world applications. However, the existing FNNS schemes are vulnerable in the sense that anyone can extract the secret from the stego-image. To deal with this issue, we propose a key-based FNNS scheme to improve the security of the FNNS, where we generate key-controlled perturbations from the FNN for data embedding. As such, only the receiver who possesses the key is able to correctly extract the secret from the stego-image using the FNN. In order to improve the visual quality and undetectability of the stego-image, we further propose an adaptive perturbation optimization strategy by taking the perturbation cost into account. Experimental results show that our proposed scheme is capable of preventing unauthorized secret extraction from the stego-images. Furthermore, our scheme is able to generate stego-images with higher visual quality than the state-of-the-art FNNS scheme, especially when the FNN is a neural network for ordinary learning tasks.

翻译：图像隐写术是一种以隐蔽方式在图像中隐藏秘密信息、使其不被未授权方察觉的技术。最新研究表明，可借助固定神经网络（FNN）实现秘密信息的嵌入与提取。这种固定神经网络隐写术（FNNS）无需训练网络即可达到高隐写性能，在实际应用中更具潜力。然而，现有FNNS方案存在安全性隐患——任何人都能从隐写图像中提取秘密信息。针对该问题，我们提出一种基于密钥的FNNS改进方案，通过从FNN生成密钥控制的扰动实现数据嵌入，从而使仅拥有密钥的接收方能够利用FNN从隐写图像中正确提取秘密信息。为进一步提升隐写图像的视觉质量与不可检测性，我们提出一种考虑扰动代价的自适应扰动优化策略。实验结果表明，所提出的方案能有效阻止未经授权的秘密信息提取。同时，与现有最先进的FNNS方案相比，本方案可生成视觉质量更高的隐写图像，尤其在FNN为普通学习任务的神经网络时效果更为显著。