Instance-Aware Encoder Adaptation for Robust Image Watermarking

Current deep watermarking frameworks typically consist of an encoder, a noise layer, and a decoder (E-N-D), in which jointly optimize the encoder and decoder over a large training set. However, this learned global embedding strategy compromises across diverse images, leaving the embedding potential of individual images under-exploited and introducing an inherent amortization gap. To address this issue, a novel paradigm termed Instance-Aware Encoder Adaptation (IAEA) is proposed in this letter. Built upon the global model trained in the first stage, IAEA freezes the decoder and fine-tunes only the encoder for each cover image and the to-be-embedded watermark in the second stage. This transforms the global optimization into an instance-level refinement under practical decoding constraints, effectively narrowing the amortization gap. The proposed IAEA can exploit the embedding potential of individual images and can be integrated with existing E-N-D methods for performance enhancement, and its effectiveness is experimentally verified.