Gradient Masking

Gradient masking is a phenomenon where modifications to model training or architecture obscure or alter the gradients used during optimization, impacting the effectiveness of gradient-based attacks and potentially improving model robustness. Current research focuses on leveraging gradient masking for defense against adversarial examples and improving the efficiency and generalization of large language and other deep learning models, often employing techniques like masking, clipping, or regularization within various architectures including convolutional and spiking neural networks. Understanding and quantifying the extent of gradient masking is crucial for evaluating the true robustness of machine learning models and developing more reliable and secure systems.