Results 81 to 90 of about 1,662,189 (295)
Spatially Transformed Adversarial Examples
CoRR, 2018 Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the $\mathcal{L}_p$ distance for penalizing perturbations.
Chaowei Xiao +5 more
openaire +4 more sources
Algebraic adversarial attacks on explainability models
Machine Learning. EngineeringClassical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated.
Lachlan Simpson +5 more
doaj +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Adversarial Examples from Dimensional Invariance [PDF]
, 2023 Adversarial examples have been found for various deep as well as shallow learning models, and have at various times been suggested to be either fixable model-specific bugs, or else inherent dataset feature, or both.
Badger, Benjamin L.
core +1 more source
Solid Harmonic Wavelet Bispectrum for Image Analysis
Advanced Science, EarlyView.The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown +3 more
wiley +1 more source
Adversarial examples - some insights
, 2021 Recent advancements in the field of deep learning have substantially increased the adoption rate of automated systems in everyday life. However, since their inception, these systems have been criticized for their lack of interpretability: it is often ...
Van Messem, Arnout
core
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Revisiting model fairness via adversarial examples
, 2023 Existing research literally evaluates model fairness over limited observed data. In practice, however, factors such as maliciously crafted examples and naturally corrupted examples often appear in real-world data collection.
Zhang, T +9 more
core +1 more source
CybersecurityMost of the adversarial attacks against speech recognition systems focus on specific adversarial perturbations, which are generated by adversaries for each normal example to achieve the attack.
Zheng Sun +4 more
doaj +1 more source
A Gradual Adversarial Training Method for Semantic Segmentation
Remote SensingDeep neural networks (DNNs) have achieved great success in various computer vision tasks. However, they are susceptible to artificially designed adversarial perturbations, which limit their deployment in security-critical applications.
Yinkai Zan, Pingping Lu, Tingyu Meng
doaj +1 more source