Adversarial Attack Detection
14 papers with code • 0 benchmarks • 0 datasets
The detection of adversarial attacks.
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Latest papers with no code
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification
Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification.
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous
The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting.
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework
In this work, we propose a distributed resilient consensus framework, consisting of a pre-designed consensus protocol and distributed model predictive control (DMPC) optimization, which can help significantly reduce the requirement on the network robustness and effectively handle the general linear constrained MAS under adversarial attacks.
Multi-head Uncertainty Inference for Adversarial Attack Detection
We adopt a multi-head architecture with multiple prediction heads (i. e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI.
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
Attack-Agnostic Adversarial Detection
The growing number of adversarial attacks in recent years gives attackers an advantage over defenders, as defenders must train detectors after knowing the types of attacks, and many models need to be maintained to ensure good performance in detecting any upcoming attacks.
Btech thesis report on adversarial attack detection and purification of adverserially attacked images
A deep learning model is trained on certain training examples for various tasks such as classification, regression etc.
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing
To this end, we propose a two-level cascading classifier that combines the GAN discriminator with a binary classifier to prevent adversarial fake tasks.
Residue-Based Natural Language Adversarial Attack Detection
Many popular image adversarial detection approaches are able to identify adversarial examples from embedding feature spaces, whilst in the NLP domain existing state of the art detection approaches solely focus on input text features, without consideration of model embedding spaces.
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency
The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID).
