To generate high-quality Synthetic Aperture Radar (SAR) images, the translation of Electro-optical (EO) to SAR images has been studied in recent years. However, these methods cannot make use of other modal information, such as Light Detection and Ranging (LiDAR), to enhance their performance, due to the absence of well-registered multi-modal remote sensing data. Therefore, in this paper, we first construct an organized high-resolution real-world benchmark dataset, which comprises 479 registered multi-modal images. We then explore how to adapt the state-of-the-art method, named the query-selected attention model (QS-Attn), to make it more suitable for integrating EO and LiDAR images into real SAR image translation. Extensive experiments have been conducted to evaluate our proposed method and compare it with state-of-the-art methods. The experimental results validate the superiority and effectiveness of our approach.

The vulnerability of deep neural networks against adversarial attacks,i.e., imperceptible adversarial perturbations can easily give rise to wrong predictions, poses a huge threat to the security of their real-world deployments. In this paper, a novel Adversarial Detection method via Disentangling Natural images and Perturbations (ADDNP) is proposed. Compared to natural images that can typically be modeled by lower-dimensional subspaces or manifolds, the distributions of adversarial perturbations are much more complex,e.g., one normal example’s adversarial counterparts generated by different attack strategies can be significantly distinct. The proposed ADDNP exploits such distinct properties for the detection of adversarial attacks amongst normal examples. Specifically, we use a dual-branch disentangling framework to encode natural images and perturbations of inputs separately, followed by joint reconstruction. During inference, the reconstruction discrepancy (RD) measured in the learned latent feature space is used as an indicator of adversarial perturbations. The proposed ADDNP algorithm is evaluated on three popular datasets,i.e., CIFAR-10, CIFAR-100, andminiImageNet with increasing data complexity, across multiple popular attack strategies. Compared to the existing and state-of-the-art detection methods, ADDNP has demonstrated promising performance on adversarial detection, with significant improvements on more challenging datasets.