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 926–950 of 1808 papers

TitleStatusHype
A Generative Adversarial Attack for Multilingual Text Classifiers—0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks—0
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—0
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models—0
Improved Adversarial Training via Learned Optimizer—0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
A General Black-box Adversarial Attack on Graph-based Fake News Detectors—0
Improving adversarial robustness of deep neural networks by using semantic information—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—0
Enhancing Transferability of Adversarial Examples with Spatial Momentum—0
Improving Adversarial Transferability with Scheduled Step Size and Dual Example—0
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
Improving Network Interpretability via Explanation Consistency Evaluation—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
A Framework for Verification of Wasserstein Adversarial Robustness—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks—0
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations—0
Improving Robustness of Task Oriented Dialog Systems—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
A Framework for Understanding Model Extraction Attack and Defense—0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection—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