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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 1808 papers

TitleStatusHype
Adversarial Attack on Sentiment Classification—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models—0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Adaptive Perturbation for Adversarial Attack—0
Bandlimiting Neural Networks Against Adversarial Attacks—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
A White-Box Adversarial Attack Against a Digital Twin—0
AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs—0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Compressed models are NOT miniature versions of large models—0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
Benchmarking Adversarial Robustness—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
ABIGX: A Unified Framework for eXplainable Fault Detection and Classification—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Compressive Sensing Based Adaptive Defence Against Adversarial Images—0
Constrained Adversarial Learning for Automated Software Testing: a literature review—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
Automated Decision-based Adversarial Attacks—0
Biologically inspired protection of deep networks from adversarial attacks—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Show:102550
← PrevPage 9 of 37Next →
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