[PMW16] Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016, May). Distillation as a defense to adversarial perturbations against deep neural networks. In the 2016 IEEE Symposium on Security and Privacy (pp. 582-597). [SZS13] Szegedy, C., Zaremba, W., Sutskever,...
A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks A table of adversarial learning publications in recommender systems. This page will beperiodicallyupdated to include recent works. Please contact us if your work is not in the list. Let us ...
fooling rate and fewer iterations.When attacking LeNet5 and AlexNet respectively,the fooling rates are 100%and 89.56%.When attacking them at the same time,the fooling rate is 69.78%.FBACC method also provides a new adversarial attack method for the study of defense against adversarial attacks.do...
Adversarial Machine Learning in Recommender Systems: State of the art and Challenges In this respect, the goal of this survey is two-fold: (i) to present recent advances on AML-RS for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful...
re made public. It was the same with cryptography in the 1990s, but eventually the science settled down as people better understood the interplay between attack and defense. So while Google, Amazon, Microsoft, and Tesla have all facedadversarial ML attackson their production systems in the last...
-184-Hidden directories and les as a source of sensitive information about web application: https://medium.com/p/84e5c534e5ad -185-Hiding Registry keys with PSRe ect: https://posts.specterops.io/hiding-registry-keys-with-psreflect-b18ec5ac8353 -186-awesome-cve-poc: https://github.com/...
An effective deep learning adversarial defense method based on spatial structural constraints in embedding space ? 2024 Elsevier B.V.Deep neural networks are highly vulnerable to adversarial samples. Most existing adversarial defense methods do not consider the distri... J Miao,X Yu,Hu Z.Liu L.So...
FBACC method also provides a new adversarial attack method for the study of defense against adversarial attacks.doi:10.32604/CMC.2020.09800Deyin LiMingzhi ChengYu YangMin LeiLinfeng ShenComputers, Materials and Continua (Tech Science Press)
as usual. Integrating these pop-ups into existing agent testing environments like OSWorld and VisualWebArena leads to an attack success rate (the frequency of the agent clicking the pop-ups) of 86% on average and decreases the task success rate by 47%. Basic defense techniques such as asking...
Instead of attack transference, direct attacks provide a way of directly attacking the target model. There are many methods for generating adversarial examples for deep-learning model attacks, such as the fast-gradient sign attack [18], Jacobian-based saliency map attack [3], and Deepfool method...