Deep Image Steganography Using Transformer and Recursive Permutation

Abstract

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.

Towards Robust Image-in-Audio Deep Steganography

“…To evaluate the effect of different embedding methods on the robustness of the steganographic method, we attempted to decode smaller temporal segments of the stego signal. In our experiment, we selectively zero out the spectral content at different time frames of the stego spectrograms, simulating a scenario where some data is lost during transmission [20,40], either in large contiguous chunks or at random positions.…”

Comparative Performance Analysis Of Deep Learning-Based Image Steganography Using U-Net, V-Net, And U-Net++ Encoders

“…When it comes to printing and photos, both deep photographic steganography [17] and light field messaging (LFM] [19] are good ways to fix problems with pictures. To make the model more reliable, Chen et al (2021) [29] this person came up with a low-frequency picture DS method. Based on the study that Yin and his colleagues did, they suggested an image DS method that has a unique fine-tuning network structure.…”

Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion

“…Zhu et al [30] proposed a new image-hiding convolutional neural network based on a residual network and pixel shuffle combined with image encryption. Wang et al [13] first introduced Transformer into image steganography and achieved higher image quality. Inspired by universal adversarial examples [31], Zhang et al [19] proposed a universal deep hiding model (UDH) to explore the generation of cover-independent perturbations in order to hide a secret image in different unknown cover images.…”

“…Deeplearning-based image steganography has been applied, mostly using deep neural networks, demonstrating promising performance in terms of embedding capacity as compared to traditional methods [10]. In addition to hiding one or more images in another image of the same size [11][12][13][14], deep neural networks can also hide binary information in an image [15,16], and the embedded binary information can be transmitted in light fields [17][18][19]. Generally, the backbone network of image-to-image deep-learning-based models is an autoencoder, which is trained in an end-to-end manner.…”

“…In general, current DS methods are based on encoder-decoder networks, and the container image is obtained through the encoding network [11][12][13][14][15][16][17][18][19]. We are the first group to propose a novel DS scheme based on adversarial attacks instead of encoding networks.…”