Results 11 to 20 of about 1,662,189 (295)

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning

2022 IEEE International Conference on Image Processing (ICIP), 2022
Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes. In this paper, we demonstrate that adversarial examples can also be utilized for good to improve the performance of ...
Wu, Chao, Li, Gang, Zhang, Jie, Zhang, Lei   +3 more
core   +4 more sources

Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

Big Data and Cognitive Computing, 2020
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri, Aminollah Khormali, Milad Salem, Jiann-Shiun Yuan   +3 more
doaj   +2 more sources

Natural Adversarial Examples [PDF]

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models reliably, demonstrating that ...
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song   +4 more
openaire   +3 more sources

Developing Hessian–Free Second–Order Adversarial Examples for Adversarial Training [PDF]

International Journal of Applied Mathematics and Computer Science
Recent studies show that deep neural networks (DNNs) are extremely vulnerable to elaborately designed adversarial examples. Adversarial training, which uses adversarial examples as training data, has been proven to be one of the most effective methods of
Qian Yaguan, Zhang Liangjian, Wang Yuqi, Ji Boyuan, Yao Tengteng, Wang Bin   +5 more
doaj   +2 more sources

Enhancing Adversarial Defense via Brain Activity Integration Without Adversarial Examples. [PDF]

Sensors (Basel)
Adversarial attacks on large-scale vision–language foundation models, such as the contrastive language–image pretraining (CLIP) model, can significantly degrade performance across various tasks by generating adversarial examples that are ...
Nakajima T, Maeda K, Togo R, Ogawa T, Haseyama M.   +4 more
europepmc   +2 more sources

Distinguishability of adversarial examples [PDF]

Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020
Machine learning models can be easily fooled by adversarial examples which are generated from clean examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection. From a unique angle, we propose
Yi Qin, Ryan Hunt, Chuan Yue
openaire   +1 more source

Verifying the Causes of Adversarial Examples [PDF]

2020 25th International Conference on Pattern Recognition (ICPR), 2021
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial ...
Li, H, Fan, Y, Ganz, F, Yezzi, A, Barnaghi, P   +4 more
openaire   +4 more sources

Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT [PDF]

, 2021
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan, Buchanan, William J., Alexios Mylonas, Mylonas, Alexios, Pavlos Papadopoulos, Pitropakis, Nikolaos, Thornewill Von Essen, Oliver, Papadopoulos, Pavlos, Chrysoulas, Christos, Essen, Oliver Thornewill von, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Chrysoulas, Christos; id_orcid   +13 more
core   +1 more source

Efficient Adversarial Training With Transferable Adversarial Examples [PDF]

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training.
Haizhong Zheng, Ziqi Zhang 0004, Juncheng Gu, Honglak Lee, Atul Prakash 0001   +4 more
openaire   +3 more sources

Adversarial examples for models of code [PDF]

Proceedings of the ACM on Programming Languages, 2020
Neural models of code have shown impressive results when performing tasks such as predicting method names and identifying certain kinds of bugs. We show that these models are vulnerable to adversarial examples , and introduce a novel approach for attacking trained models of code using ...
Noam Yefet, Uri Alon 0002, Eran Yahav
openaire   +4 more sources