Robustness to adversarial examples can be improved with overfitting
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-020-01097-4
Abstract: Deep learning (henceforth DL) has become most powerful machine learning methodology. Under specific circumstances recognition rates even surpass those obtained by humans. Despite this, several works have shown that deep learning produces outputs that are… read more here.
Keywords: robustness adversarial; machine learning; improved overfitting; adversarial examples ... See more keywords
Strengthening Robustness Under Adversarial Attacks Using Brain Visual Codes
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3204995
Abstract: The vulnerability of computational models to adversarial examples highlights the differences in the ways humans and machines process visual information. Motivated by human perception invariance in object recognition, we aim to incorporate human brain representations… read more here.
Keywords: adversarial attacks; brain; using brain; attacks using ... See more keywords
On the Relationship between Generalization and Robustness to Adversarial Examples
Sign Up to like & getrecommendations! Published in 2021 at "Symmetry"
DOI: 10.3390/sym13050817
Abstract: One of the most intriguing phenomenons related to deep learning is the so-called adversarial examples. These samples are visually equivalent to normal inputs, undetectable for humans, yet they cause the networks to output wrong results.… read more here.
Keywords: robustness adversarial; generalization; relationship generalization; generalization robustness ... See more keywords