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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 301–310 of 1808 papers

Title	Date	Tasks	Status	Hype
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances	Nov 17, 2020	Adversarial AttackSpeaker Identification	CodeCode Available	1
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking	Jan 1, 2020	Adversarial AttackAutonomous Driving	CodeCode Available	1
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection	Oct 29, 2020	Adversarial AttackFace Detection	CodeCode Available	1
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model	Jan 11, 2024	Adversarial Attack	CodeCode Available	1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape	Apr 24, 2024	Adversarial AttackFace Swapping	CodeCode Available	1
An Efficient Adversarial Attack for Tree Ensembles	Oct 22, 2020	Adversarial Attackvalid	CodeCode Available	1
Adversarial Attack and Defense in Deep Ranking	Jun 7, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Geometric Adversarial Attacks and Defenses on 3D Point Clouds	Dec 10, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
GreedyFool: Distortion-Aware Sparse Adversarial Attack	Oct 26, 2020	Adversarial Attack	CodeCode Available	1
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics	Nov 1, 2022	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available	1

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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