Adversarial attacks and adversarial robustness in computational pathology [PDF]
Nature Communications, 2022 Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in ...
Narmin Ghaffari Laleh +10 more
doaj +8 more sources
Adversarial Robustness with Partial Isometry [PDF]
EntropyDespite their remarkable performance, deep learning models still lack robustness guarantees, particularly in the presence of adversarial examples. This significant vulnerability raises concerns about their trustworthiness and hinders their deployment in ...
Loïc Shi-Garrier +2 more
doaj +5 more sources
Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation [PDF]
SensorsDespite their high prediction accuracy, deep learning-based soft sensor (DLSS) models face challenges related to adversarial robustness against malicious adversarial attacks, which hinder their widespread deployment and safe application.
Runyuan Guo +3 more
doaj +3 more sources
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing [PDF]
Frontiers in Artificial Intelligence, 2022 Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. In this paper, we develop improved techniques for defending
Xingjian Li +4 more
doaj +2 more sources
Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification [PDF]
Sensors, 2023 Deep neural networks (DNNs) have been known to be vulnerable to adversarial attacks. Adversarial training (AT) is, so far, the only method that can guarantee the robustness of DNNs to adversarial attacks.
Desheng Wang, Weidong Jin, Yunpu Wu
doaj +2 more sources
Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary [PDF]
PLoS ONE, 2022 There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task.
Shashank Kotyan +1 more
doaj +3 more sources
Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training
TechnologiesThe adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth
Anna Chistyakova +6 more
doaj +3 more sources
Adversarial robustness guarantees for quantum classifiers [PDF]
npj Quantum InformationDespite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data.
Neil Dowling +6 more
doaj +2 more sources
The inherent adversarial robustness of analog in-memory computing [PDF]
Nature CommunicationsA key challenge for deep neural network algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on analog in-memory computing, have been speculated to provide significant adversarial ...
Corey Lammie +4 more
doaj +2 more sources
Active machine learning approach to adversarial training improves trade-off between natural accuracy and adversarial robustness [PDF]
Scientific ReportsUnderstanding our world which is open and diverse requires foundation models that generalize well while trustworthy. Adversarial training has been considered to be one of the most effective strategies to achieve robust learning systems, yet adversarial ...
Seyed Mohammad Hadi Mirsadeghi
doaj +2 more sources