Search

Traditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks. [ABSTRACT FROM AUTHOR]

Accurate segmentation of brain tumors has a vital impact on clinical diagnosis and treatment, and good segmentation results are helpful for the treatment of this disease, which is a serious threat to human health. High‐precision segmentation of brain tumors remains a challenging task due to their diverse shapes, sizes, locations, and complex boundaries. Considering the special structure of medical brain tumor images, many researchers have proposed a brain tumor segmentation (BraTS) network based on 3D U‐Net. However, there are also problems such as insufficient receptive fields and excessive computing costs. In this paper, we propose an efficient BraTS model based on group normalization (GN) and 3D U‐Net (3D‐EffUNet). First, according to the characteristics of brain tumor images, the medical image of the whole case is input into the model, and 3D convolution layers are used to extract features and filter irrelevant information. Then, using 3D U‐Net as the main framework, an efficient convolutional module is designed for more precise processing of brain tumor features. Moreover, an efficient convolution module based on GN and an attention mechanism is introduced to reduce the complexity of the network without affecting the segmentation performance and to increase the awareness of voxels between adjacent dimensions and the local space. Finally, the decoder was used to reconstruct high‐precision BraTS information. The model is trained and tested on the BraTS2021 dataset, and the experimental results show that it can maintain good segmentation performance and greatly reduce the calculation cost. [ABSTRACT FROM AUTHOR]

Generative steganography is a research hotspot particularly with regard to information hiding, which involves the hiding of secret information by generating sufficiently "real" secret media. In recent years, generative steganography schemes have made significant progress in images, but the field of video steganography is still in the exploratory stage. Combined with deep convolutional generative adversarial nets (DCGAN), a semi-generative video steganography scheme based on a digital Carden Grille was proposed. A dual-stream video generation network based on DCGAN was designed to generate three parts of videos: foreground, background, and mask; generation network produced different videos with random noise. The digital Carden Grille is used as the key for embedding and extraction. The sender can generate a digital Carden Grille in the mask through two different methods, reasonably assign the embedding capacity among RGB channels, and use video pixels as the carrier to achieve the information embedding in a semi-generative way. The receiver can determine the embedding position of information through the Carden Grille and extract the secret information hidden in the pixels. Experimental results show that the stego video generated by this scheme has good visual quality, with a Frechet inception distance score of 92. The embedded capacity is better than the existing generative steganography schemes, up to 0.12 bpp. Using Syndrome Trellis Coding, the proposed scheme can transmit secret messages more efficiently and securely. [ABSTRACT FROM AUTHOR]

In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method. [ABSTRACT FROM AUTHOR]