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 1251–1275 of 1808 papers

TitleStatusHype
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
GradMDM: Adversarial Attack on Dynamic Networks—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents—0
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm—0
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks—0
Harmonic Adversarial Attack Method—0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—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
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—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
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case—0
How Sampling Impacts the Robustness of Stochastic Neural Networks—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks—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