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To ensure protection of the intellectual property rights of DNN models,watermarking techniques have been investigated to insert side-information intothe models without seriously degrading the performance of original task. One ofthe threats for the DNN watermarking is the pruning attack such that lessimportant neurons in the model are pruned to make it faster and more compact aswell as to remove the watermark. In this study, we investigate a channel codingapproach to resist the pruning attack. As the channel model is completelydifferent from conventional models like digital images, it has been an openproblem what kind of encoding method is suitable for DNN watermarking. A novelencoding approach by using constant weight codes to immunize the effects ofpruning attacks is presented. To the best of our knowledge, this is the firststudy that introduces an encoding technique for DNN watermarking to make itrobust against pruning attacks.

Immunization of Pruning Attack in DNN Watermarking Using Constant Weight Code