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 876–900 of 1808 papers

TitleStatusHype
Improving Network Interpretability via Explanation Consistency Evaluation—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Harmonic Adversarial Attack Method—0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems—0
Biologically inspired protection of deep networks from adversarial attacks—0
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks—0
Adversarial Attack Against Images Classification based on Generative Adversarial Networks—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—0
Enhancing Transferability of Adversarial Examples with Spatial Momentum—0
An Actor-Critic Method for Simulation-Based Optimization—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