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 126–150 of 1808 papers

TitleStatusHype
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Robustness of Selected Learning Models under Label-Flipping Attack—0
Enhancing Adversarial Transferability via Component-Wise Transformation—0
Differentiable Adversarial Attacks for Marked Temporal Point ProcessesCode0
Salient Information Preserving Adversarial Training Improves Clean and Robust Accuracy—0
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework—0
Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities—0
Effective faking of verbal deception detection with target-aligned adversarial attacks—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Distillation-Enhanced Physical Adversarial Attacks—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
Enhancing Adversarial Transferability with Checkpoints of a Single Model's Training—0
I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models—0
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF AttackCode0
Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attack on Breast Ultrasound Images—0
ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector—0
Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Robustness-aware Automatic Prompt OptimizationCode0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping 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