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 426–450 of 1808 papers

TitleStatusHype
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
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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