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 826–850 of 1808 papers

TitleStatusHype
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation—0
Another Dead End for Morphological Tags? Perturbed Inputs and ParsingCode0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning—0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space—0
Attribute-Guided Encryption with Facial Texture Masking—0
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial AttackCode0
Dynamic Transformers Provide a False Sense of EfficiencyCode0
Spatial-Frequency Discriminability for Revealing Adversarial PerturbationsCode0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend—0
Content-based Unrestricted Adversarial Attack—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
Attacking Perceptual Similarity Metrics—0
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search—0
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples—0
New Adversarial Image Detection Based on Sentiment AnalysisCode0
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed FeatureCode0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Evaluating Adversarial Robustness on Document Image Classification—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods—0
Combining Generators of Adversarial Malware Examples to Increase Evasion RateCode0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality MetricsCode0
Generating Adversarial Attacks in the Latent Space—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