gradientsteg
Gradientsteg is a term that describes a technique for embedding information within the gradients of machine learning models. Instead of hiding data within the model's weights or parameters directly, gradientsteg manipulates the gradients that are calculated during the backpropagation process. This allows for a covert channel to be established, where information can be communicated or watermarked without significantly altering the model's core functionality or performance.
The core idea behind gradientsteg involves subtly modifying the gradient values in a way that is imperceptible
Applications of gradientsteg include data watermarking, where ownership or provenance of a trained model can be