Lianshan Liu

Image steganography is a process of embedding a secret image into a cover image to achieve secret transmission and accurately recovering a secret image from a stego image. To further investigate the high invisibility and extraction accuracy of steganography, this study presents RISRANet, a reversible image steganography network utilizing residual structure and mixed attention mechanism, markedly enhancing both the fidelity of stego image and the accuracy of restoring secret image. The network uses INN as the overall framework, adopts a double-branch structure, extracts deep features using the mixed attention mechanism, and employs channel shuffle to promote information interaction between different features. This paper introduces dilated convolution to design a multi-scale convolution attention module that combines feature information from different scales, highlights essential features, and precisely locates the ideal embedding position. In addition, the residual structure is constructed to allow for feature reuse, and the structural similarity is introduced into the loss function to improve the accuracy of information recovery. The experimental results suggest that the framework achieves secure hiding and lossless extraction of secret images, superior to comparative algorithms in multiple evaluation metrics.

With the development of digital imaging devices, the process of recording sensitive information displayed on screens through mobile phones and cameras has become a prominent technique for modern data leaks. In order to identify the origin of information violations, Screen-Shooting Resistant Watermarking (SSRW) has attracted a lot of attention. Most existing solutions are based on Convolutional Neural Networks (CNNs) for the embedding of watermarks. However, due to the limited reception field of CNNs, they are proficient in extracting local features but cannot understand the entire image. This paper presents a new watermarking system that is resistant to screen recording, with multi-head and cross-attention to incorporate watermarks, replacing the encoder in the end-to-end architecture. Specifically, we segment the image and watermark into smaller patches for positional embedding. Afterward, we calculate the attention scores through multi-head attention layers and generate the encoded image through concatenation. This approach increases the model’s ability to comprehend the entire image, thereby increasing performance. In addition, we enhance the U-Net network structure to replace the end-to-end decoder. The experimental results demonstrate that the proposed method not only reaches more than 95% accuracy in different capture scenarios but also excels in terms of reliability and invisibility relative to current state-of-the-art (SOTA) methods. In addition, this approach yields impressive PSNR and SSIM average values of 41.90 dB and 0.99, showing the excellent visual quality and reliability of the watermarked images.

The hiding capacity of the current information hiding field has reached a relatively high level, which can hide two color images into one color image. In order to explore a larger hidden capacity, an information hiding scheme based on an improved FCdDNet is proposed, which can hide large-size color images into small-size color images. An improved FCdDNet network is used as the main structure shared by the hidden network and the extraction network. These two networks promote and improve each other during the confrontation training process and are used in pairs. It can be seen that the proposed scheme achieves a larger information hiding capacity, and the hidden information is four times larger than the scale of the carrier image. At the same time, the visual effect after hiding is guaranteed, and the image extracted from the hidden image also has a high degree of restoration. The scheme can be applied to image authentication, secret image transmission, and other fields.

Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy of secret information. In this paper, the steganography method based on invertible neural networks is proposed, which can generate stego images with high invisibility and security and can achieve lossless recovery for secret information. In addition, this paper introduces a mapping module that can compress information actually embedded to improve the quality of the stego image and its antidetection ability. In order to restore message and prevent loss, the secret information is converted into a binary sequence and then embedded in the cover image through the forward operation of the invertible neural networks. This information will then be recovered from the stego image through the inverse operation of the invertible neural networks. Experimental results show that the proposed method in this paper has achieved competitive results in the visual quality and safety of stego images and achieved 100% accuracy in information extraction.

With the in-depth research in data hiding technology, the latest data hiding schemes using deep neural network (DNN) could hide the full-size color image into the same size color image. This paper proposed a larger capacity data hiding scheme based on DNN, which could hide large-size color image into small-size color image. This scheme used DNN in both the data hiding stage and data extraction stage. And the two networks were obtained through adversarial training and used in pairs. The information ratio between the carrier image and the secret image had reached 1 : 4. The image obtained after hiding the data did not change much compared with the carrier image, and the extracted image had the same size and no visual difference compared with the secret image. It could be seen from the simulation experiment that, compared with the existing DNN-based data hiding scheme, even if the capacity of the proposed scheme was 4 times larger than that of the existing scheme, the visual effect and PSNR were also better than some existing schemes.

With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.