1,357 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
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
Radar is of vital importance in many fields, such as autonomous driving,
safety and surveillance applications. However, it suffers from stringent
constraints on its design parametrization leading to multiple trade-offs. For
example, the bandwidth in FMCW radars is inversely proportional with both the
maximum unambiguous range and range resolution. In this work, we introduce a
new method for circumventing radar design trade-offs. We propose the use of
recent advances in computer vision, more specifically generative adversarial
networks (GANs), to enhance low-resolution radar acquisitions into higher
resolution counterparts while maintaining the advantages of the low-resolution
parametrization. The capability of the proposed method was evaluated on the
velocity resolution and range-azimuth trade-offs in micro-Doppler signatures
and FMCW uniform linear array (ULA) radars, respectively.Comment: Accepted in EUSIPCO 2019, 5 page
Physics-Informed Computer Vision: A Review and Perspectives
Incorporation of physical information in machine learning frameworks are
opening and transforming many application domains. Here the learning process is
augmented through the induction of fundamental knowledge and governing physical
laws. In this work we explore their utility for computer vision tasks in
interpreting and understanding visual data. We present a systematic literature
review of formulation and approaches to computer vision tasks guided by
physical laws. We begin by decomposing the popular computer vision pipeline
into a taxonomy of stages and investigate approaches to incorporate governing
physical equations in each stage. Existing approaches in each task are analyzed
with regard to what governing physical processes are modeled, formulated and
how they are incorporated, i.e. modify data (observation bias), modify networks
(inductive bias), and modify losses (learning bias). The taxonomy offers a
unified view of the application of the physics-informed capability,
highlighting where physics-informed learning has been conducted and where the
gaps and opportunities are. Finally, we highlight open problems and challenges
to inform future research. While still in its early days, the study of
physics-informed computer vision has the promise to develop better computer
vision models that can improve physical plausibility, accuracy, data efficiency
and generalization in increasingly realistic applications
Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures
Deep learning methods are often used for image classification or local object segmentation. The corresponding test and validation data sets are an integral part of the learning process and also of the algorithm performance evaluation. High and particularly very high-resolution Earth observation (EO) applications based on satellite images primarily aim at the semantic labeling of land cover structures or objects as well as of temporal evolution classes. However, one of the main EO objectives is physical parameter retrievals such as temperatures, precipitation, and crop yield predictions. Therefore, we need reliably labeled data sets and tools to train the developed algorithms and to assess the performance of our deep learning paradigms. Generally, imaging sensors generate a visually understandable representation of the observed scene. However, this does not hold for many EO images, where the recorded images only depict a spectral subset of the scattered light field, thus generating an indirect signature of the imaged object. This spots the load of EO image understanding, as a new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). This chapter reviews and analyses the new approaches of EO imaging leveraging the recent advances in physical process-based ML and AI methods and signal processing
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