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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 221–230 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art	Dec 23, 2021	Adversarial AttackMalware Detection	CodeCode Available	1	5
Certifying LLM Safety against Adversarial Prompting	Sep 6, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	1	5
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data	Jun 2, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1	5
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes	May 23, 2021	Adversarial AttackFace Swapping	CodeCode Available	1	5
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples	Jul 21, 2023	Adversarial AttackAdversarial Attack Detection	CodeCode Available	1	5
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Composite Adversarial Attacks	Dec 10, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Are AlphaZero-like Agents Robust to Adversarial Perturbations?	Nov 7, 2022	Adversarial AttackBoard Games	CodeCode Available	1	5

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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