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 726–750 of 1808 papers

TitleStatusHype
Evaluating Adversarial Robustness on Document Image Classification—0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach—0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication—0
Evaluating Neural Model Robustness for Machine Comprehension—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—0
Democratic Training Against Universal Adversarial Perturbations—0
Evaluating the Robustness of LiDAR Point Cloud Tracking Against Adversarial Attack—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
Analyzing the Noise Robustness of Deep Neural Networks—0
Delving into Data: Effectively Substitute Training for Black-box Attack—0
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System—0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search—0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
Defensive Quantization: When Efficiency Meets Robustness—0
EvolBA: Evolutionary Boundary Attack under Hard-label Black Box condition—0
Adversarial Attack with Raindrops—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition—0
Attacking Perceptual Similarity Metrics—0
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—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