To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

Generative steganography (GS) generates stego-media via secret messages, but existing GS only targets single-type multimedia data with poor universality. The generator and extractor sizes are highly coupled with resolution. Message mapping converts secret messages and noise, yet current GS schemes based on it use gridded data, failing to generate diverse multimedia universally. Inspired by implicit neural representation (INR), we propose generative implicit steganography via message mapping (GIS). We designed single-bit and multi-bit message mapping schemes in function domains. The scheme’s function generator eliminates the coupling between model and gridded data sizes, enabling diverse multimedia generation and breaking resolution limits. A dedicated point cloud extractor is trained for adaptability. Through a literature review, this scheme is the first to perform message mapping in the functional domain. During the experiment, taking images as an example, methods such as PSNR, StegExpose, and neural pruning were used to demonstrate that the generated image quality is almost indistinguishable from the real image. At the same time, the generated image is robust. The accuracy of message extraction can reach 96.88% when the embedding capacity is 1 bpp, 89.84% when the embedding capacity is 2 bpp, and 82.21% when the pruning rate is 0.3.

Deep neural networks (DNNs) usually have thousands of trainable parameters to ensure high accuracy. Due to large amounts of computation and memory requirements, these networks are not suitable for real-time and resource-constrained systems. Various techniques such as network pruning, weight sharing, network quantization, and weight encoding have improved computational and memory efficiency. The synchronous weight quantization-compression (SWQC) technique applies both network quantization and weight encoding to realize weight compression in the process of network quantization. This technique generates a quantized neural network (QNN) model with a good trade-off between accuracy and compression rate by choosing the proper group size, retraining epoch number, and weight threshold. To further improve the compression rate of SWQC, a new strategy for weight encoding, unbalanced encoding, is proposed in this paper. This strategy is able to compress one or multiple quantized weights into one bit, thereby achieving a higher compression rate. Experiments are performed on a 4-bit QNN using the CIFAR10 dataset. The results show that unbalanced encoding achieves a higher compression rate for the layers with large-quantity parameters. By using mixed encoding which combines balanced and unbalanced encoding in different layers can achieve a higher compression rate than using one of them only. In the experiments with CIFAR10, unbalanced encoding gets the compression rate of over 13X in the fully connected layer. By comparison, the compression rate of SWQC with the incorporation of unbalanced encoding achieves more than 5X higher than using balanced encoding only.