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 651–675 of 1808 papers

TitleStatusHype
Boosting Adversarial Transferability using Dynamic Cues—0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training—0
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item DetectionCode1
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements—0
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot LearningCode1
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
Graph Adversarial Immunization for Certifiable RobustnessCode0
Threatening Patch Attacks on Object Detection in Optical Remote Sensing ImagesCode0
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend—0
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification—0
TransFool: An Adversarial Attack against Neural Machine Translation ModelsCode0
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks—0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
Improving Adversarial Transferability with Scheduled Step Size and Dual Example—0
Identifying Adversarially Attackable and Robust SamplesCode0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Semantic Adversarial Attacks on Face Recognition through Significant Attributes—0
Targeted Attacks on Timeseries Forecasting—0
Attacking Important Pixels for Anchor-free Detectors—0
On the Adversarial Robustness of Camera-based 3D Object DetectionCode1
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics—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