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 1201–1250 of 1808 papers

TitleStatusHype
Differential Privacy in Personalized Pricing with Nonparametric Demand Models—0
Energy Attack: On Transferring Adversarial Examples—0
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
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
Real-World Adversarial Examples involving Makeup Application—0
Excess Capacity and Backdoor PoisoningCode0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
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
Detecting and Segmenting Adversarial Graphics Patterns from Images—0
Application of Adversarial Examples to Physical ECG Signals—0
Adversarial Relighting Against Face Recognition—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Optical Adversarial Attack—0
Deep adversarial attack on target detection systems—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
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
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition—0
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based AttacksCode0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam DetectionCode0
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial AttacksCode0
Noise-based cyberattacks generating fake P300 waves in brain–computer interfacesCode0
Learning to Detect Adversarial Examples Based on Class Scores—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural NetworksCode0
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples—0
In-distribution adversarial attacks on object recognition models using gradient-free searchCode0
Bio-Inspired Adversarial Attack Against Deep Neural Networks—0
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Attack to Fool and Explain Deep Networks—0
Limited Budget Adversarial Attack Against Online Image Stream—0
Light Lies: Optical Adversarial Attack—0
Is It Time to Redefine the Classification Task for Deep Learning Systems?—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions—0
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology—0
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models—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