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 401–425 of 1746 papers

Title	Date	Tasks	Status	Hype
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection	Feb 26, 2024	Adversarial Robustness	—Unverified	0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection	Feb 25, 2024	Adversarial RobustnessCyber Attack Detection	—Unverified	0
Spectrum Extraction and Clipping for Implicitly Linear Layers	Feb 25, 2024	Adversarial Robustness	CodeCode Available	0
Distilling Adversarial Robustness Using Heterogeneous Teachers	Feb 23, 2024	Adversarial RobustnessKnowledge Distillation	—Unverified	0
On the Duality Between Sharpness-Aware Minimization and Adversarial Training	Feb 23, 2024	Adversarial Robustness	CodeCode Available	1
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing	Feb 23, 2024	Adversarial Robustness	—Unverified	0
Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial Image	Feb 22, 2024	Adversarial RobustnessMultimodal Reasoning	CodeCode Available	1
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment	Feb 21, 2024	Adversarial RobustnessBenchmarking	CodeCode Available	1
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions	Feb 20, 2024	Adversarial RobustnessEvolutionary Algorithms	—Unverified	0
Evaluating Adversarial Robustness of Low dose CT Recovery	Feb 18, 2024	Adversarial RobustnessComputed Tomography (CT)	CodeCode Available	0
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models	Feb 18, 2024	Adversarial RobustnessAdversarial Text	CodeCode Available	0
Maintaining Adversarial Robustness in Continuous Learning	Feb 17, 2024	Adversarial RobustnessContinual Learning	—Unverified	0
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation	Feb 16, 2024	Adversarial RobustnessDomain Adaptation	—Unverified	0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models	Feb 16, 2024	Adversarial RobustnessLanguage Modelling	—Unverified	0
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion	Feb 14, 2024	Adversarial RobustnessDomain Generalization	CodeCode Available	0
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space	Feb 14, 2024	Adversarial RobustnessSafety Alignment	CodeCode Available	1
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring	Feb 13, 2024	Adversarial RobustnessDecision Making	—Unverified	0
Two Tales of Single-Phase Contrastive Hebbian Learning	Feb 13, 2024	Adversarial Robustness	CodeCode Available	0
Tighter Bounds on the Information Bottleneck with Application to Deep Learning	Feb 12, 2024	Adversarial RobustnessDeep Learning	CodeCode Available	0
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense	Feb 11, 2024	Adversarial Robustnessimage-classification	—Unverified	0
RAMP: Boosting Adversarial Robustness Against Multiple l_p Perturbations for Universal Robustness	Feb 9, 2024	Adversarial Robustness	CodeCode Available	0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation	Feb 9, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
Is Adversarial Training with Compressed Datasets Effective?	Feb 8, 2024	Adversarial RobustnessDataset Condensation	CodeCode Available	0
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs	Feb 8, 2024	Adversarial Robustness	—Unverified	0
Adversarial Robustness Through Artifact Design	Feb 7, 2024	Adversarial RobustnessTraffic Sign Recognition	—Unverified	0

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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