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 826–850 of 1808 papers

TitleStatusHype
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving—0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Biologically inspired protection of deep networks from adversarial attacks—0
Bio-Inspired Adversarial Attack Against Deep Neural Networks—0
Adversarial Attacks against Deep Saliency Models—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
Adversarial Robustness through Dynamic Ensemble Learning—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
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Adversarial Relighting Against Face Recognition—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Generating Semantic Adversarial Examples via Feature Manipulation—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