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 651–675 of 1808 papers

TitleStatusHype
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques—0
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
Design of secure and robust cognitive system for malware detection—0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks—0
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack—0
Double Backpropagation for Training Autoencoders against Adversarial Attack—0
Democratic Training Against Universal Adversarial Perturbations—0
Do we need entire training data for adversarial training?—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Analyzing the Noise Robustness of Deep Neural Networks—0
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
DTA: Physical Camouflage Attacks using Differentiable Transformation Network—0
Delving into Data: Effectively Substitute Training for Black-box Attack—0
Applying Tensor Decomposition to image for Robustness against Adversarial Attack—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
Adversarial Attack with Raindrops—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications—0
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition—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