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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 1311–1320 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack on Network Embeddings via Supervised Network Poisoning	Feb 14, 2021	Adversarial AttackLink Prediction	CodeCode Available	0
Adversarially robust deepfake media detection using fused convolutional neural network predictions	Feb 11, 2021	Adversarial AttackDeepFake Detection	—Unverified	0
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes	Feb 10, 2021	Adversarial AttackInference Attack	CodeCode Available	0
RoBIC: A benchmark suite for assessing classifiers robustness	Feb 10, 2021	Adversarial Attack	CodeCode Available	0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples	Feb 8, 2021	Adversarial Attack	—Unverified	0
Audio Adversarial Examples: Attacks Using Vocal Masks	Feb 4, 2021	Adversarial AttackSpeech-to-Text	—Unverified	0
Improving Neural Network Robustness through Neighborhood Preserving Layers	Jan 28, 2021	Adversarial Attack	—Unverified	0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method	Jan 26, 2021	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models	Jan 26, 2021	3D Object DetectionAdversarial Attack	—Unverified	0
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems	Jan 26, 2021	Activity RecognitionAdversarial Attack	—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