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A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation. [PDF]
PLoS OneKirubakaran M, Mohan J.
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A data independent approach to generate adversarial patches
Machine Vision and Applications, 2021Deep neural networks are vulnerable to adversarial examples, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently, adversarial patch, with perturbations confined to a small and localized patch, emerged for its easy accessibility in real-world attack.
Xingyu Zhou 0002 +4 more
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Enhancing the Transferability of Adversarial Examples with Random Patch
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features of objects to generate adversarial examples.
Yaoyuan Zhang +5 more
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Naturalistic Physical Adversarial Patch for Object Detectors
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches. This leads to conspicuous and attention-grabbing patterns for the generated patches which can be easily identified by humans. To address this issue, we pro-pose a method to
Hu, Y.-C.-T. +5 more
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2017
Deep learning algorithms have gained a lot of popularity in recent years due to their state-of-the-art results in computer vision applications. Despite their success, studies have shown that neural networks are vulnerable to attacks via perturbations in input images in various forms, called adversarial examples.
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Deep learning algorithms have gained a lot of popularity in recent years due to their state-of-the-art results in computer vision applications. Despite their success, studies have shown that neural networks are vulnerable to attacks via perturbations in input images in various forms, called adversarial examples.
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Scaling Resilient Adversarial Patch
2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2021Yunhong Yin +4 more
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