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Found 4 papers, 3 papers with code
Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization
However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima.
MTTM: Metamorphic Testing for Textual Content Moderation Software
In addition, we leverage the test cases generated by MTTM to retrain the model we explored, which largely improves model robustness (0% to 5. 9% EFR) while maintaining the accuracy on the original test set.
Local Low-light Image Enhancement via Region-Aware Normalization
RANLEN uses a dynamically designed mask-based normalization operation, which enhances an image in a spatially varying manner, ensuring that the enhancement results are consistent with the requirements specified by the input mask.
Improving Adversarial Transferability via Neuron Attribution-Based Attacks
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples.
