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Found 5 papers, 3 papers with code
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload.
Federated Adversarial Training with Transformers
Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studied for convolutional neural network (CNN).
Reveal of Vision Transformers Robustness against Adversarial Attacks
For instance, we found that 1) Vanilla ViTs or hybrid-ViTs are more robust than CNNs under Lp-based attacks and under adaptive attacks.
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison
In this paper, we focus on image classification task and attempt to provide a survey for detection methods of test-time evasion attacks on neural network classifiers.
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection
Moreover, the state-of-the-art detection techniques have been designed for specific attacks or broken by others, need knowledge about the attacks, are not consistent, increase model parameters overhead, are time-consuming, or have latency in inference time.
