  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.
