A well-defined cost function is a key issue for image steganography to minimize the embedding distortion. In recent years, deep learning has been introduced into image steganography to automatically learn embedding costs and improve steganographic security. For most existing generative adversarial network (GAN) based cost learning works, the generator usually adopts an encoder-decoder architecture. However, due to repeated encoding and decoding operations, this architecture is prone to information loss, making the generator difficult to well capture fine-grained image features. In this work, w...
