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 1126–1150 of 1808 papers

TitleStatusHype
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Towards Security Threats of Deep Learning Systems: A Survey—0
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks—0
Towards Robustness of Deep Neural Networks via Regularization—0
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning—0
Towards Robust Speech-to-Text Adversarial Attack—0
Towards Sybil Resilience in Decentralized Learning—0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods—0
Towards Transferable Adversarial Attack against Deep Face Recognition—0
Towards Transferable Adversarial Attacks with Centralized Perturbation—0
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks—0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models—0
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence—0
Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images—0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning—0
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation—0
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients—0
Transferable Adversarial Attack on Image Tampering Localization—0
Transferable Adversarial Examples for Anchor Free Object Detection—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
Transferable Learned Image Compression-Resistant Adversarial Perturbations—0
Transferable Perturbations of Deep Feature Distributions—0
Transferable Physical Attack against Object Detection with Separable Attention—0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms—0
Trustworthy Actionable Perturbations—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