Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 326–350 of 1746 papers

Title	Date	Tasks	Status
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks	May 13, 2019	Adversarial RobustnessIntrusion Detection	—Unverified
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI	May 29, 2024	Adversarial Robustness	—Unverified
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection	Feb 25, 2024	Adversarial RobustnessCyber Attack Detection	—Unverified
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models	Apr 24, 2024	Adversarial RobustnessQuantum Machine Learning	—Unverified
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness	Mar 1, 2021	Adversarial RobustnessObject Recognition	—Unverified
Adversarial Robustness Across Representation Spaces	Dec 1, 2020	Adversarial Robustnessimage-classification	—Unverified
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs	Dec 13, 2022	Adversarial RobustnessFake News Detection	—Unverified
A More Biologically Plausible Local Learning Rule for ANNs	Nov 24, 2020	Adversarial RobustnessBinary Classification	—Unverified
A margin-based replacement for cross-entropy loss	Jan 21, 2025	Adversarial RobustnessContinual Learning	—Unverified
Adversarial Attacks and Defenses for Speech Recognition Systems	Mar 31, 2021	Adversarial RobustnessAutomatic Speech Recognition	—Unverified
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders	May 6, 2025	Adversarial Robustness	—Unverified
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation	Jun 17, 2021	Adversarial RobustnessFairness	—Unverified
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks	Feb 15, 2018	Adversarial Robustness	—Unverified
aiXamine: Simplified LLM Safety and Security	Apr 21, 2025	2kAdversarial Robustness	—Unverified
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems	Nov 9, 2024	Adversarial Robustnessimage-classification	—Unverified
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs	May 16, 2025	Adversarial RobustnessSafety Alignment	—Unverified
Conflict-Aware Adversarial Training	Oct 21, 2024	Adversarial Robustness	—Unverified
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models	Oct 18, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
A Holistic Assessment of the Reliability of Machine Learning Systems	Jul 20, 2023	Adversarial RobustnessOut-of-Distribution Detection	—Unverified
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs	Feb 8, 2024	Adversarial Robustness	—Unverified
A Closer Look at the Adversarial Robustness of Information Bottleneck Models	Jul 12, 2021	Adversarial Robustness	—Unverified
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks	Oct 10, 2023	Adversarial Robustness	—Unverified
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification	Nov 6, 2024	Adversarial Robustnessregression	—Unverified
A Frequency Perspective of Adversarial Robustness	Oct 26, 2021	Adversarial Robustness	—Unverified
Adversarial Prompt Distillation for Vision-Language Models	Nov 22, 2024	Adversarial RobustnessAutonomous Driving	—Unverified

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified