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 651–675 of 1808 papers

TitleStatusHype
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification—0
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing—0
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper—0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated PoliciesCode0
An Adversarial Approach to Evaluating the Robustness of Event Identification Models—0
AICAttack: Adversarial Image Captioning Attack with Attention-Based OptimizationCode0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
Corruption Robust Offline Reinforcement Learning with Human Feedback—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks—0
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision TransformersCode0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on TextCode0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
AdvGPS: Adversarial GPS for Multi-Agent Perception AttackCode0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Sparse and Transferable Universal Singular Vectors Attack—0
Exploring Adversarial Threat Models in Cyber Physical Battery Systems—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