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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1291–1300 of 1808 papers

Title	Date	Tasks	Status	Hype
Bridging the Performance Gap between FGSM and PGD Adversarial Training	Nov 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Single-Node Attacks for Fooling Graph Neural Networks	Nov 6, 2020	Adversarial Attack	CodeCode Available	1
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks	Nov 5, 2020	Adversarial Attack	—Unverified	0
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty	Nov 5, 2020	Adversarial AttackBenchmarking	CodeCode Available	0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA	Nov 5, 2020	Adversarial AttackCloud Computing	CodeCode Available	0
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks	Nov 3, 2020	Adversarial AttackMachine Translation	CodeCode Available	0
Second-Order NLP Adversarial Examples	Nov 1, 2020	Adversarial AttackSemantic Similarity	—Unverified	0
TextAttack: Lessons learned in designing Python frameworks for NLP	Nov 1, 2020	Adversarial AttackData Augmentation	—Unverified	0
Generalization to Mitigate Synonym Substitution Attacks	Nov 1, 2020	Adversarial Attack	—Unverified	0
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries	Nov 1, 2020	Adversarial AttackDeep Learning	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified