A benchmarking protocol for SAR colorization: From regression to deep learning approaches.

Synthetic aperture radar (SAR) images are widely used in remote sensing. Interpreting SAR images can be challenging due to their intrinsic speckle noise and grayscale nature. To address this issue, SAR colorization has emerged as a research direction to colorize gray scale SAR images while preserving the original spatial information and radiometric information. However, this research field is still in its early stages, and many limitations can be highlighted. In this paper, we propose a full research line for supervised learning-based approaches to SAR colorization. Our approach includes a protocol for generating synthetic color SAR images, several baselines, and an effective method based on the conditional generative adversarial network (cGAN) for SAR colorization. We also propose numerical assessment metrics for the problem at hand. To our knowledge, this is the first attempt to propose a research line for SAR colorization that includes a protocol, a benchmark, and a complete performance evaluation. Our extensive tests demonstrate the effectiveness of our proposed cGAN-based network for SAR colorization. The code is available at https://github.com/shenkqtx/SAR-Colorization-Benchmarking-Protocol.

Adversarial feature hybrid framework for steganography with shifted window local loss.

Image steganography is a long-standing image security problem that aims at hiding information in cover images. In recent years, the application of deep learning to steganography has the tendency to outperform traditional methods. However, the vigorous development of CNN-based steganalyzers still have a serious threat to steganography methods. To address this gap, we present an end-to-end adversarial steganography framework based on CNN and Transformer learned by shifted window local loss, called StegoFormer, which contains Encoder, Decoder, and Discriminator. Encoder is a hybrid model based on U-shaped network and Transformer block, which effectively integrates high-resolution spatial features and global self-attention features. In particular, Shuffle Linear layer is suggested, which can enhance the linear layer's competence to extract local features. Given the substantial error in the central patch of the stego image, we propose shifted window local loss learning to assist Encoder in generating accurate stego images via weighted local loss. Furthermore, Gaussian mask augmentation method is designed to augment data for Discriminator, which helps to improve the security of Encoder through adversarial training. Controlled experiments show that StegoFormer is superior to the existing advanced steganography methods in terms of anti-steganalysis ability, steganography effectiveness, and information restoration.