178 research outputs found
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
Deep neural networks (DNNs) have achieved tremendous success in many remote
sensing (RS) applications, in which DNNs are vulnerable to adversarial
perturbations. Unfortunately, current adversarial defense approaches in RS
studies usually suffer from performance fluctuation and unnecessary re-training
costs due to the need for prior knowledge of the adversarial perturbations
among RS data. To circumvent these challenges, we propose a universal
adversarial defense approach in RS imagery (UAD-RS) using pre-trained diffusion
models to defend the common DNNs against multiple unknown adversarial attacks.
Specifically, the generative diffusion models are first pre-trained on
different RS datasets to learn generalized representations in various data
domains. After that, a universal adversarial purification framework is
developed using the forward and reverse process of the pre-trained diffusion
models to purify the perturbations from adversarial samples. Furthermore, an
adaptive noise level selection (ANLS) mechanism is built to capture the optimal
noise level of the diffusion model that can achieve the best purification
results closest to the clean samples according to their Frechet Inception
Distance (FID) in deep feature space. As a result, only a single pre-trained
diffusion model is needed for the universal purification of adversarial samples
on each dataset, which significantly alleviates the re-training efforts and
maintains high performance without prior knowledge of the adversarial
perturbations. Experiments on four heterogeneous RS datasets regarding scene
classification and semantic segmentation verify that UAD-RS outperforms
state-of-the-art adversarial purification approaches with a universal defense
against seven commonly existing adversarial perturbations. Codes and the
pre-trained models are available online (https://github.com/EricYu97/UAD-RS).Comment: Added the GitHub link to the abstrac
Experimental study on water evaporation from sand using environmental chamber
International audienceLarge-scale evaporation experiments were conducted on bare sand using an environmental chamber. Four different atmospheric conditions and various drying durations were imposed to soil sample. Both the atmospheric parameters (air flow rate, relative humidity and temperature) and the response of soil (volumetric water content, temperature and soil suction) were monitored simultaneously. Notably, the temperature and matric suction at soil surface were monitored using infrared thermometer and high-capacity tensiometer, respectively. The results show that the air and soil temperatures depend on the evaporation process and atmospheric conditions. In addition, volumetric water content in the near-surface zone is strongly affected by the evaporation process and changes linearly over depth. The evaporation rate is strongly dependent on the air conditions
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly
intensified research in the geoscience and remote sensing (RS) field. AI
algorithms, especially deep learning-based ones, have been developed and
applied widely to RS data analysis. The successful application of AI covers
almost all aspects of Earth observation (EO) missions, from low-level vision
tasks like super-resolution, denoising and inpainting, to high-level vision
tasks like scene classification, object detection and semantic segmentation.
While AI techniques enable researchers to observe and understand the Earth more
accurately, the vulnerability and uncertainty of AI models deserve further
attention, considering that many geoscience and RS tasks are highly
safety-critical. This paper reviews the current development of AI security in
the geoscience and RS field, covering the following five important aspects:
adversarial attack, backdoor attack, federated learning, uncertainty and
explainability. Moreover, the potential opportunities and trends are discussed
to provide insights for future research. To the best of the authors' knowledge,
this paper is the first attempt to provide a systematic review of AI
security-related research in the geoscience and RS community. Available code
and datasets are also listed in the paper to move this vibrant field of
research forward
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