FPR manipulation attack perturbs benign MQTT packets to flip labels to attacks in NIDS with 80-100% success, increasing SOC delays without gradient-based methods.
Recent advances in adversarial training for adversarial robustness
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
citation-role summary
background 4
citation-polarity summary
roles
background 4polarities
background 4representative citing papers
Rotationally equivariant quantum models can rely on vulnerable invariant statistics such as ring-averaged intensities, leaving them susceptible to classical transfer attacks, but suppressing the associated symmetry sectors substantially improves robustness.
An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.
Identifies sensitivity as the source of both discriminability and vulnerability in FC classifiers versus robustness in l2 classifiers, and introduces HPM prototype fusion plus MSA evaluation to improve adversarial robustness.
CoNewsReader integrates user comments with an LLM to improve critical news reading on social media, with a 24-participant study showing gains in comprehension and critical thinking over baseline interfaces.
DACO curates a 15,000-concept dictionary from 400K image-caption pairs and uses it to initialize an SAE that enables granular, concept-specific steering of MLLM activations, raising safety scores on MM-SafetyBench and JailBreakV while preserving general capabilities.
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
A survey that organizes machine unlearning verification methods into behavioral and parametric categories and outlines open problems.
Personalized federated learning shows heightened vulnerability to transfer-based adversarial attacks from malicious clients, addressed by a defense framework of stochastic input noise, input-scaled trace regularization, and parameter sensitivity maximization.
Auto-ART delivers the first structured synthesis of adversarial robustness consensus plus an executable multi-norm testing framework that flags gradient masking in 92% of cases on RobustBench and reveals a 23.5 pp robustness gap.
citing papers explorer
-
Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks
FPR manipulation attack perturbs benign MQTT packets to flip labels to attacks in NIDS with 80-100% success, increasing SOC delays without gradient-based methods.
-
Feature-level analysis and adversarial transfer in rotationally equivariant quantum machine learning
Rotationally equivariant quantum models can rely on vulnerable invariant statistics such as ring-averaged intensities, leaving them susceptible to classical transfer attacks, but suppressing the associated symmetry sectors substantially improves robustness.
-
Improving Feasibility via Fast Autoencoder-Based Projections
An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.
-
Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
Identifies sensitivity as the source of both discriminability and vulnerability in FC classifiers versus robustness in l2 classifiers, and introduces HPM prototype fusion plus MSA evaluation to improve adversarial robustness.
-
Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs
DACO curates a 15,000-concept dictionary from 400K image-caption pairs and uses it to initialize an SAE that enables granular, concept-specific steering of MLLM activations, raising safety scores on MM-SafetyBench and JailBreakV while preserving general capabilities.
-
Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
-
Towards Reliable Forgetting: A Survey on Machine Unlearning Verification
A survey that organizes machine unlearning verification methods into behavioral and parametric categories and outlines open problems.
-
Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design
Personalized federated learning shows heightened vulnerability to transfer-based adversarial attacks from malicious clients, addressed by a defense framework of stochastic input noise, input-scaled trace regularization, and parameter sensitivity maximization.
-
Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing
Auto-ART delivers the first structured synthesis of adversarial robustness consensus plus an executable multi-norm testing framework that flags gradient masking in 92% of cases on RobustBench and reveals a 23.5 pp robustness gap.