  Static deep neural network (DNN) watermarking techniques typically employ
irreversible methods to embed watermarks into the DNN model weights. However,
this approach causes permanent damage to the watermarked model and fails to
meet the requirements of integrity authentication. Reversible data hiding (RDH)
methods offer a potential solution, but existing approaches suffer from
weaknesses in terms of usability, capacity, and fidelity, hindering their
practical adoption. In this paper, we propose a novel RDH-based static DNN
watermarking scheme using quantization index modulation (QIM). Our scheme
incorporates a novel approach based on a one-dimensional quantizer for
watermark embedding. Furthermore, we design two schemes to address the
challenges of integrity protection and legitimate authentication for DNNs.
Through simulation results on training loss and classification accuracy, we
demonstrate the feasibility and effectiveness of our proposed schemes,
highlighting their superior adaptability compared to existing methods.
