Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution. [PDF]
Entropy (Basel), 2023 Li S +5 more
europepmc +1 more source
VectorDefense: Vectorization as a Defense to Adversarial Examples
CoRR, 2018 Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples. Inspired by how humans reconstruct abstract concepts, we attempt to codify the input bitmap image into a set of compact, interpretable elements to avoid being fooled by the adversarial structures.
Vishaal Munusamy Kabilan +2 more
openaire +2 more sources
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
CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking
DronesVisual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance.
Ruilong Yu +5 more
doaj +1 more source
Adversarial training and deep k-nearest neighbors improves adversarial defense of glaucoma severity detection. [PDF]
Heliyon, 2022 Riza Rizky LM, Suyanto S.
europepmc +1 more source
Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting [PDF]
, 2021 Federico Nesti +2 more
openalex +1 more source
Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi‐agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity.
Yirui Liu +6 more
wiley +1 more source
Time-Constrained Adversarial Defense in IoT Edge Devices through Kernel Tensor Decomposition and Multi-DNN Scheduling. [PDF]
Sensors (Basel), 2022 Kim M, Joo S.
europepmc +1 more source
From Ambiguous Queries to Verifiable Insights: A Task‐Driven Framework for LLM‐Powered SOC Analysis⋆
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Security operations centre (SOC) analysts must investigate alerts, correlate threat intelligence and interpret heterogeneous telemetry under tight timing constraints. Although large language models (LLMs) offer strong understanding capabilities, directly applying them to SOC environments remains challenging due to semantic ambiguity in analyst
Huan Zhang +5 more
wiley +1 more source
Detection and Defense: Student-Teacher Network for Adversarial Robustness
IEEE AccessDefense against adversarial attacks is critical for the reliability and safety of deep neural networks (DNNs). Current state-of-the-art defense methods achieve significant robustness against adversarial attacks.
Kyoungchan Park, Pilsung Kang
doaj +1 more source