Backdoor attacks for remote sensing data with wavelet transform
Recent years have witnessed the great success of deep learning algorithms in the
geoscience and remote sensing (RS) realm. Nevertheless, the security and robustness of …
geoscience and remote sensing (RS) realm. Nevertheless, the security and robustness of …
Local context normalization: Revisiting local normalization
Normalization layers have been shown to improve convergence in deep neural networks,
and even add useful inductive biases. In many vision applications the local spatial context of …
and even add useful inductive biases. In many vision applications the local spatial context of …
Adversarial attacks against a satellite-borne multispectral cloud detector
Data collected by Earth-observing (EO) satellites are often afflicted by cloud cover. Detecting
the presence of clouds—which is increasingly done using deep learning—is crucial …
the presence of clouds—which is increasingly done using deep learning—is crucial …
Integrating deep learning-based data driven and model-based approaches for inverse synthetic aperture radar target recognition
We explore the blending of model-based and deep learning approaches for target
recognition in inverse synthetic aperture radar (ISAR) imagery. It evaluates five different …
recognition in inverse synthetic aperture radar (ISAR) imagery. It evaluates five different …
Generating natural adversarial remote sensing images
Over the last years, remote sensing image (RSI) analysis has started resorting to using deep
neural networks to solve most of the commonly faced problems, such as detection, land …
neural networks to solve most of the commonly faced problems, such as detection, land …
Optimizing honey traffic using game theory and adversarial learning
Enterprises are increasingly concerned about adversaries that slowly and deliberately
exploit resources over the course of months or even years. A key step in this kill chain is …
exploit resources over the course of months or even years. A key step in this kill chain is …
The risk and opportunity of adversarial example in military field
Y Chen - Proceedings of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
Artificial intelligence technology is increasingly widely used in the military field, and various
countries have carried out a number of research and experiments, aiming to use artificial …
countries have carried out a number of research and experiments, aiming to use artificial …
A realistic approach for network traffic obfuscation using adversarial machine learning
Adversaries are becoming more sophisticated and standard countermeasures such as
encryption are no longer enough to prevent traffic analysis from revealing important …
encryption are no longer enough to prevent traffic analysis from revealing important …
Impact of architecture on robustness and interpretability of multispectral deep neural networks
Including information from additional spectral bands (eg, near-infrared) can improve deep
learning model performance for many vision-oriented tasks. There are many possible ways …
learning model performance for many vision-oriented tasks. There are many possible ways …
Impact of model architecture on robustness and interpretability of multispectral deep learning models
C Godfrey, E Bishoff, M McKay… - … , and Applications for …, 2023 - spiedigitallibrary.org
Including information from additional spectral bands (eg, near-infrared) can improve deep
learning model performance for many vision-oriented tasks. There are many possible ways …
learning model performance for many vision-oriented tasks. There are many possible ways …