295 research outputs found
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques
SSGAN: Secure Steganography Based on Generative Adversarial Networks
In this paper, a novel strategy of Secure Steganograpy based on Generative
Adversarial Networks is proposed to generate suitable and secure covers for
steganography. The proposed architecture has one generative network, and two
discriminative networks. The generative network mainly evaluates the visual
quality of the generated images for steganography, and the discriminative
networks are utilized to assess their suitableness for information hiding.
Different from the existing work which adopts Deep Convolutional Generative
Adversarial Networks, we utilize another form of generative adversarial
networks. By using this new form of generative adversarial networks,
significant improvements are made on the convergence speed, the training
stability and the image quality. Furthermore, a sophisticated steganalysis
network is reconstructed for the discriminative network, and the network can
better evaluate the performance of the generated images. Numerous experiments
are conducted on the publicly available datasets to demonstrate the
effectiveness and robustness of the proposed method
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