Results 141 to 150 of about 85,909 (246)
Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
Sparse attacks are to optimize the magnitude of adversarial perturbations for fooling deep neural networks (DNNs) involving only a few perturbed pixels (i.e., under the l0 constraint), suitable for interpreting the vulnerability of DNNs. However, existing solutions fail to yield interpretable adversarial examples due to their poor sparsity. Worse still,
Lin, Fudong +4 more
openaire +2 more sources
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Networked control systems (NCSs) often suffer from performance degradation due to limited communication bandwidth, which can cause data transmission conflicts and packet loss. Existing scheduling strategies may fail to simultaneously meet the real‐time requirements and the importance of multisensor data, and they are particularly vulnerable ...
Da Chen +5 more
wiley +1 more source
Untargeted white-box adversarial attack to break into deep leaning based COVID-19 monitoring face mask detection system. [PDF]
Multimed Tools Appl, 2023 Sheikh BUH, Zafar A.
europepmc +1 more source
Robustness Against Adversarial Attacks Via Learning Confined Adversarial Polytopes
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Comment: The paper has been accepted in ICASSP ...
Hamidi, Shayan Mohajer, Ye, Linfeng
openaire +2 more sources
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim +4 more
wiley +1 more source
An improved genetic algorithm and its application in neural network adversarial attack. [PDF]
PLoS One, 2022 Yang D, Yu Z, Yuan H, Cui Y.
europepmc +1 more source
A Probability‐Aware AI Framework for Reliable Anti‐Jamming Communication
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐hopping techniques. Nevertheless, these techniques become vulnerable when facing intelligent jammers.
Tawfeeq Shawly, Ahmed A. Alsheikhy
wiley +1 more source
How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA). [PDF]
Biomedicines, 2022 Maliamanis TV +2 more
europepmc +1 more source
AT‐AER: Adversarial Training With Adaptive Example Reuse
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Adversarial training (AT) is widely regarded as a crucial defense method for deep neural networks against adversarial attacks. Most of the existing AT methods suffer from the problems of insufficient coverage of perturbation space and robust overfitting.
Meng Hu +5 more
wiley +1 more source
Boosting Adversarial Transferability Through Adversarial Attack Enhancer
Applied SciencesAdversarial attacks against deep learning models achieve high performance in white-box settings but often exhibit low transferability in black-box scenarios, especially against defended models. In this work, we propose Multi-Path Random Restart (MPRR), which initializes multiple restart points with random noise to optimize gradient updates and improve ...
Wenli Zeng, Hong Huang, Jixin Chen
openaire +1 more source