In addition, this article uses knowledge distillation to improve the traditional adversarial training defense, which improves the robustness of the model. Simulation results show that the proposed attack and defense methods have better performance than traditional methods. 展开 ...
[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,...
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)
We are amazed by real-world demonstrations of adversarial attacks on ML systems, such as a 3D-printed object that looks like a turtle but is recognized (from any orientation) by the ML system as agun. Or adding a few stickers that look like smudges to a stop sign so that it is recog...
-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/...
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 ...
a novel geometric perspective explaining universal adversarial attacks on large language models. By attacking the 117M parameter GPT-2 model, we find evidence indicating that universal adversarial triggers could be embedding vectors which merely approximate the semantic information in their adversarial ...
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...
-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/...
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...