Papers Adversarial Attack Detection
“Adversarial Attack Detection” 태그가 달린 논문 38편 · 필터 해제
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
Deep neural networks (DNNs) are highly susceptible to adversarial examples--subtle, imperceptible perturbations that can lead to incorrect predictions. While detection-based defenses offer a practical alternative to adve…
Adversarial Attack DetectionAdversarial DefenseUnleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection
Adversarial attacks pose a critical security threat to real-world AI systems by injecting human-imperceptible perturbations into benign samples to induce misclassification in deep learning models. While existing detectio…
Adversarial AttackAdversarial Attack DetectionAnomaly DetectionFeature Engineering+2ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is genera…
Adversarial AttackAdversarial Attack DetectionFace SwappingSpeaker Verification+5Neural Fingerprints for Adversarial Attack Detection
Deep learning models for image classification have become standard tools in recent years. A well known vulnerability of these models is their susceptibility to adversarial examples. These are generated by slightly alteri…
Adversarial AttackAdversarial Attack DetectionAdversarial DefenseAdversarial Robustness+4DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies
Recent advancements in deep learning, particularly in medical imaging, have significantly propelled the progress of healthcare systems. However, examining the robustness of medical images against adversarial attacks is c…
Adversarial AttackAdversarial Attack DetectionDiagnosticSelf-Supervised Representation Learning for Adversarial Attack Detection
Supervised learning-based adversarial attack detection methods rely on a large number of labeled data and suffer significant performance degradation when applying the trained model to new domains. In this paper, we propo…
Adversarial AttackAdversarial Attack DetectionRepresentation LearningConformal 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. However, Deep learning also present risks and vulnera…
Adversarial AttackAdversarial Attack DetectionClassificationDeep Learning+1Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous
Research on developing deep learning techniques for autonomous spacecraft relative navigation challenges is continuously growing in recent years. Adopting those techniques offers enhanced performance. However, such appro…
Adversarial AttackAdversarial Attack DetectionPose EstimationResilient and constrained consensus against adversarial attacks: A distributed MPC framework
There has been a growing interest in realizing the resilient consensus of the multi-agent system (MAS) under cyber-attacks, which aims to achieve the consensus of normal agents (i.e., agents without attacks) in a network…
Adversarial AttackAdversarial Attack DetectionModel Predictive ControlOUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
Large Language Models (LLMs) have achieved human-level fluency in text generation, making it difficult to distinguish between human-written and LLM-generated texts. This poses a growing risk of misuse of LLMs and demands…
Adversarial AttackAdversarial Attack DetectionDeepFake DetectionIn-Context Learning+4Graph-based methods coupled with specific distributional distances for adversarial attack detection
Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These \textit{adversarial} attacks have been the focus of extensive research. Likewise, there…
Adversarial AttackAdversarial Attack DetectionClassificationMulti-head Uncertainty Inference for Adversarial Attack Detection
Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), hav…
Adversarial AttackAdversarial Attack DetectionAdversarial DefenseUnfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been c…
Adversarial Attack DetectionAdversarial DefenseAdversarial RobustnessBinary Classification+1Benchmarking 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. Despite their accuracy and sophistication, neural networks can be easily fooled…
Adversarial AttackAdversarial Attack DetectionAutonomous VehiclesBenchmarking+1Detecting Adversarial Examples in Batches -- a geometrical approach
Many deep learning methods have successfully solved complex tasks in computer vision and speech recognition applications. Nonetheless, the robustness of these models has been found to be vulnerable to perturbed inputs or…
Adversarial AttackAdversarial Attack DetectionImage ClassificationAttack-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…
Adversarial AttackAdversarial Attack DetectionAnomaly DetectionBtech thesis report on adversarial attack detection and purification of adverserially attacked images
This is Btech thesis report on 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. By train…
Adversarial AttackAdversarial Attack DetectionBIG-bench Machine LearningUncertainty Estimation of Transformer Predictions for Misclassification Detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of th…
Active LearningAdversarial AttackAdversarial Attack DetectionClassification+8Residue-Based Natural Language Adversarial Attack Detection
Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction. However, to date the majority of the approaches to detect these attacks ha…
Adversarial AttackAdversarial Attack DetectionSentenceSentence Embedding+1Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing
Mobile Crowdsensing systems are vulnerable to various attacks as they build on non-dedicated and ubiquitous properties. Machine learning (ML)-based approaches are widely investigated to build attack detection systems and…
Adversarial AttackAdversarial Attack DetectionGenerative Adversarial Network