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 1051–1100 of 1808 papers

TitleStatusHype
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs—0
Fooling Adversarial Training with Induction Noise—0
One for Many: an Instagram inspired black-box adversarial attack—0
Rethinking Adversarial Transferability from a Data Distribution Perspective—0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial AttackCode0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Robust Physical-World Attacks on Face Recognition—0
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial AttackCode1
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification ModelsCode1
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—0
Differential Privacy in Personalized Pricing with Nonparametric Demand Models—0
Energy Attack: On Transferring Adversarial Examples—0
Multi-granularity Textual Adversarial Attack with Behavior CloningCode1
Protein Folding Neural Networks Are Not Robust—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
Training Meta-Surrogate Model for Transferable Adversarial AttackCode0
Real-World Adversarial Examples involving Makeup Application—0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
Excess Capacity and Backdoor PoisoningCode0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural NetworksCode1
Disrupting Adversarial Transferability in Deep Neural NetworksCode0
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
Application of Adversarial Examples to Physical ECG Signals—0
Detecting and Segmenting Adversarial Graphics Patterns from Images—0
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Adversarial Relighting Against Face Recognition—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Optical Adversarial Attack—0
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Deep adversarial attack on target detection systems—0
Meta Gradient Adversarial AttackCode1
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Poison Ink: Robust and Invisible Backdoor AttackCode1
On the Robustness of Domain Adaption to Adversarial Attacks—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Benign Adversarial Attack: Tricking Models for Goodness—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—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