Results 81 to 90 of about 86,248 (260)
Leveraging Attack Non-Transferability to Boost Adversarial Robustness for Foundation Models
Applied SciencesThis paper presents a novel adversarial defense framework that strategically exploits the non-transferability of adversarial attacks across multi-modal foundation models.
Koshiro Toishi +4 more
doaj +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
Robust Adversarial Reinforcement Learning
, 2017 Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning ...
Pinto, Lerrel +3 more
openaire +2 more sources
Bioinspired Morphology‐Decoupled Soft Gripper with Enhanced Bidirectional Grasping Capability
Advanced Science, EarlyView.Inspired by biological predation, a novel gripper decouples cross‐modal grasping via dual morphological configurations. Synergistically integrating hybrid rigid and soft coupled fingers with a metamaterial palm, the system performs active compliant grasping for static objects and passive cage capture for dynamic targets.
Yedong Huang +4 more
wiley +1 more source
Remote SensingArtificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture.
Sumaiya Tasneem, Kazi Aminul Islam
doaj +1 more source
Enhancing Adversarial Robustness through Stable Adversarial Training
SymmetryDeep neural network models are vulnerable to attacks from adversarial methods, such as gradient attacks. Evening small perturbations can cause significant differences in their predictions. Adversarial training (AT) aims to improve the model’s adversarial robustness against gradient attacks by generating adversarial samples and optimizing the ...
Kun Yan +3 more
openaire +1 more source
Advanced Science, EarlyView.SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Pareto adversarial robustness: balancing spatial robustness and sensitivity-based robustness
Science China Information SciencesAdversarial robustness, which primarily comprises sensitivity-based robustness and spatial robustness, plays an integral part in achieving robust generalization. In this paper, we endeavor to design strategies to achieve universal adversarial robustness.
Sun, Ke, Li, Mingjie, Lin, Zhouchen
openaire +2 more sources
Synthesizing Robust Adversarial Examples
, 2017 Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems.
Athalye, Anish +3 more
openaire +2 more sources