Abstract
Image hiding has received significant attention due to the need of enhanced multimedia services such as multimedia security and meta-information embedding for multimedia augmentation. Recently, deep learning-based methods have been introduced that are capable of significantly increasing the hidden capacity and supporting full-size image hiding. However, these methods suffer from the necessity to balance the errors of the modified cover image and the recovered hidden image. In this paper, we propose a novel joint compressive autoencoder (J-CAE) framework to design an image hiding algorithm that achieves full-size image hidden capacity with small reconstruction errors of the hidden image. More importantly, our approach addresses the trade-off problem of previous deep learning-based methods by mapping the image representations in the latent spaces of the joint CAE models. Thus, both visual quality of the container image and recovery quality of the hidden image can be simultaneously improved. Extensive experimental results demonstrate that our proposed method outperforms several state-of-the-art deep learning-based image hiding techniques in terms of imperceptibility and recovery quality of the hidden images while maintaining full-size image hidden capacity.
Highlights
Image hiding has become one of the most important information hiding technologies to embed secret messages in multimedia data
deep learning (DL)-based full-image-to-image hiding algorithms need to learn to balance the residuals between container images and cover images, and the reconstruction errors of the hidden images since these two terms form the constraints in the proposed frameworks
The number of images used as cover images and hidden images are set both including 4000 training images and 2000 test images
Summary
Image hiding has become one of the most important information hiding technologies to embed secret messages in multimedia data. The main contributions of our work can be highlighted as follows: (i) our proposed image hiding algorithm fundamentally avoids the quality trade-off problem via joint deep autoencoder networks, providing excellent performance in both high hidden capacity and human imperceptibility. To our knowledge, this is the first approach to solve the quality tradeoff problem for effective full-image-to-image hiding, (ii) the compressive autoencoder approach ensures high quality recovery of the hidden image and (iii) a logistic-logistic chaotic mechanism is employed for the mapping of representations in the latent spaces to further enhance the image hiding security.
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