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 1076–1100 of 1808 papers

TitleStatusHype
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Vulnerability of Appearance-based Gaze Estimation—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems—0
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors—0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Towards a Novel Perspective on Adversarial Examples Driven by Frequency—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping—0
Minority Reports Defense: Defending Against Adversarial Patches—0
Adversarial training with perturbation generator networks—0
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune—0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
Mixed Strategies for Security Games with General Defending Requirements—0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks—0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks—0
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios—0
Model Extraction Warning in MLaaS Paradigm—0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Modeling Biological Immunity to Adversarial Examples—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