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 851–900 of 1808 papers

TitleStatusHype
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
GradMDM: Adversarial Attack on Dynamic Networks—0
To be Robust and to be Fair: Aligning Fairness with Robustness—0
Class-Conditioned Transformation for Enhanced Robust Image ClassificationCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Effective black box adversarial attack with handcrafted kernels—0
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and ChallengesCode0
Semantic Image Attack for Visual Model Diagnosis—0
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face RecognitionCode0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems—0
Wasserstein Adversarial Examples on Univariant Time Series Data—0
Revisiting DeepFool: generalization and improvementCode0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense—0
Translate your gibberish: black-box adversarial attack on machine translation systemsCode0
NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models—0
Resilient Dynamic Average Consensus based on Trusted agents—0
Constrained Adversarial Learning for Automated Software Testing: a literature review—0
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?Code0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
Feature Unlearning for Pre-trained GANs and VAEs—0
Do we need entire training data for adversarial training?—0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups—0
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient EstimationCode0
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit CalibrationCode0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models—0
Targeted Adversarial Attacks against Neural Machine TranslationCode0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems—0
Adversarial Attack with Raindrops—0
Contextual adversarial attack against aerial detection in the physical world—0
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks—0
Boosting Adversarial Transferability using Dynamic Cues—0
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System—0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements—0
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
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
Identifying Adversarially Attackable and Robust SamplesCode0
Improving Adversarial Transferability with Scheduled Step Size and Dual Example—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