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Investigation of the Robustness and Transferability of Adversarial Patches in Multi-View Infrared Target Detection. [PDF]
J ImagingZhou Q +7 more
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FortiNIDS: Defending Smart City IoT Infrastructures Against Transferable Adversarial Poisoning in Machine Learning-Based Intrusion Detection Systems. [PDF]
Sensors (Basel)Alajaji A.
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FORT-IDS: a federated, optimized, robust and trustworthy intrusion detection system for IIoT security. [PDF]
Sci RepMazroa AA.
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Boosting adversarial robustness via self-paced adversarial training
Neural Networks, 2023Adversarial training is considered one of the most effective methods to improve the adversarial robustness of deep neural networks. Despite the success, it still suffers from unsatisfactory performance and overfitting. Considering the intrinsic mechanism of adversarial training, recent studies adopt the idea of curriculum learning to alleviate ...
Lirong He +5 more
openaire +2 more sources
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
British Machine Vision Conference, 2021Convolutional Neural Networks (CNNs) have become the de facto gold standard in computer vision applications in the past years. Recently, however, new model architectures have been proposed challenging the status quo.
Philipp Benz
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Benchmarking Adversarial Robustness on Image Classification
Computer Vision and Pattern Recognition, 2020Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
Yinpeng Dong +6 more
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Trading Inference-Time Compute for Adversarial Robustness
arXiv.orgWe conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks.
Wojciech Zaremba +10 more
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Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
arXiv.orgMulti-modal Large Language Models (MLLMs) have recently achieved enhanced performance across various vision-language tasks including visual grounding capabilities. However, the adversarial robustness of visual grounding remains unexplored in MLLMs.
Kuofeng Gao +4 more
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Artificial Intelligence and Cloud Computing Conference
With the increasing demand for practical applications of Large Language Models (LLMs), many attention-efficient models have been developed to balance performance and computational cost.
Xiaojing Fan, Chunliang Tao
semanticscholar +1 more source
With the increasing demand for practical applications of Large Language Models (LLMs), many attention-efficient models have been developed to balance performance and computational cost.
Xiaojing Fan, Chunliang Tao
semanticscholar +1 more source