Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [PDF]
Computer Vision and Pattern Recognition, 2022 This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous approaches discover commons underlying the two modalities and fuse upon the ...
Jinyuan Liu +6 more
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RADAR: Robust AI-Text Detection via Adversarial Learning [PDF]
Neural Information Processing Systems, 2023 Recent advances in large language models (LLMs) and the intensifying popularity of ChatGPT-like applications have blurred the boundary of high-quality text generation between humans and machines.
Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho
semanticscholar +1 more source
Survey of Adversarial Attacks and Defense Methods for Deep Learning Model [PDF]
Jisuanji gongcheng, 2021 As an important part of artificial intelligence technology,deep learning is widely used in computer vision,natural language processing and other fields.Although deep learning performs well in tasks such as image classification and target detection,its ...
JIANG Yan, ZHANG Liguo
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Learning new attack vectors from misuse cases with deep reinforcement learning
Frontiers in Energy Research, 2023 Modern smart grids already consist of various components that interleave classical Operational Technology (OT) with Information and Communication Technology (ICT), which, in turn, have opened the power grid to advanced approaches using distributed ...
Eric M. S. P. Veith +2 more
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Adversarial attacks on deep learning models in smart grids
Energy Reports, 2022 A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
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DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis [PDF]
Computer Vision and Pattern Recognition, 2022 Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging.
F. Haghighi +3 more
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Adversarial Self-Supervised Learning for Robust SAR Target Recognition
Remote Sensing, 2021 Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years.
Yanjie Xu +5 more
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Adversarial attacks against supervised machine learning based network intrusion detection systems.
PLoS ONE, 2022 Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani +3 more
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Sensors, 2023 Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes.
Mohammed Alkhowaiter +4 more
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TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation [PDF]
Knowledge-Based Systems, 2022 Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the ...
Sungho Suh, V. F. Rey, P. Lukowicz
semanticscholar +1 more source