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 1151–1175 of 1808 papers

TitleStatusHype
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples—0
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks—0
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets—0
Undersensitivity in Neural Reading Comprehension—0
Understanding Model Ensemble in Transferable Adversarial Attack—0
Understanding Oversmoothing in GNNs as Consensus in Opinion Dynamics—0
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Universal Adversarial Attack on Aligned Multimodal LLMs—0
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Universal Adversarial Attack Using Very Few Test Examples—0
Universal Adversarial Perturbations and Image Spam Classifiers—0
Universal Attacks on Equivariant Networks—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks—0
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification—0
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation—0
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models—0
Untargeted Adversarial Attack on Knowledge Graph Embeddings—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—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