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 1676–1700 of 1808 papers

TitleStatusHype
Snowball Adversarial Attack on Traffic Sign Classification—0
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions—0
Universal Attacks on Equivariant Networks—0
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Detecting and Segmenting Adversarial Graphics Patterns from Images—0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Adversarial Attack on Facial Recognition using Visible Light—0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
Device-aware Optical Adversarial Attack for a Portable Projector-camera System—0
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies—0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Architecture Selection via the Trade-off Between Accuracy and Robustness—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement—0
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications—0
Differentially Private Reward Estimation with Preference Feedback—0
Differential Privacy in Personalized Pricing with Nonparametric Demand Models—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Applying Tensor Decomposition to image for Robustness against Adversarial Attack—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