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 601–625 of 1808 papers

TitleStatusHype
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
A Non-monotonic Smooth Activation Function—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation—0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
Evaluating Adversarial Robustness on Document Image Classification—0
Evaluations and Methods for Explanation through Robustness Analysis—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Adversarial defenses via a mixture of generators—0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training—0
Adversarial Defense based on Structure-to-Signal Autoencoders—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks—0
Adversarial Data Encryption—0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms—0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs—0
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models—0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
Adversarial Body Shape Search for Legged Robots—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features—0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks—0
Device-aware Optical Adversarial Attack for a Portable Projector-camera System—0
Detecting and Segmenting Adversarial Graphics Patterns from Images—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