Results 111 to 120 of about 177,286 (215)

Generating Image Adversarial Examples by Embedding Digital Watermarks [PDF]

, 2020
Yuexin Xiang, Tiantian Li, Wei Ren, Tianqing Zhu, Kim‐Kwang Raymond Choo   +4 more
openalex   +1 more source

BeamAttack: Generating High-quality Textual Adversarial Examples through Beam Search and Mixed Semantic Spaces [PDF]

, 2023
Hai Zhu, Zhao, Qingyang, Yuren Wu
openalex   +1 more source

Universal adversarial defense in remote sensing based on pre-trained denoising diffusion models

International Journal of Applied Earth Observations and Geoinformation
Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO ...
Weikang Yu, Yonghao Xu, Pedram Ghamisi
doaj   +1 more source

Generating adversarial examples without specifying a target model. [PDF]

PeerJ Comput Sci, 2021
Yang G, Li M, Fang X, Zhang J, Liang X.
europepmc   +1 more source

Towards Interpreting and Utilizing Symmetry Property in Adversarial Examples

, 2023
Shibin Mei, Chenglong Zhao, Bingbing Ni, Shengchao Yuan   +3 more
openalex   +2 more sources

Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples. [PDF]

Entropy (Basel), 2021
Mahmood K, Gurevin D, van Dijk M, Nguyen PH.   +3 more
europepmc   +1 more source

Regularized adversarial examples for model interpretability [PDF]

, 2018
Yoel Shoshan, Vadim Ratner
openalex   +1 more source

Improving the Transferability of Adversarial Examples With a Noise Data Enhancement Framework and Random Erasing. [PDF]

Front Neurorobot, 2021
Xie P, Shi S, Yang S, Qiao K, Liang N, Wang L, Chen J, Hu G, Yan B.   +8 more
europepmc   +1 more source

Adversarial Examples-Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices. [PDF]

IEEE Internet Things J, 2021
Rahman A, Hossain MS, Alrajeh NA, Alsolami F.   +3 more
europepmc   +1 more source

Adversarial Examples Are Not Bugs, They Are Superposition

CoRR
Adversarial examples -- inputs with imperceptible perturbations that fool neural networks -- remain one of deep learning's most perplexing phenomena despite nearly a decade of research. While numerous defenses and explanations have been proposed, there is no consensus on the fundamental mechanism.
Liv Gorton, Owen Lewis
openaire   +2 more sources