SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

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Showing 351–400 of 1808 papers

TitleStatusHype
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
ADMM based Distributed State Observer Design under Sparse Sensor Attacks—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Adversarial training with perturbation generator networks—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition—0
A Brief Survey on Deep Learning Based Data Hiding—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator—0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
Adversarial Attacks against Deep Saliency Models—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Adversarial Relighting Against Face Recognition—0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
Adversarial Attack on Sentiment Classification—0
A Black-Box Attack on Optical Character Recognition Systems—0
Brightness-Restricted Adversarial Attack Patch—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs—0
Adaptive Perturbation for Adversarial Attack—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified