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Digital watermarking has been widely studied for the protection ofintellectual property. Traditional watermarking schemes often design in a"wider" rule, which applies one general embedding mechanism to all images. Butthis will limit the scheme into a robustness-invisibility trade-off, where theimprovements of robustness can only be achieved by the increase of embeddingintensity thus causing the visual quality decay. However, a new scenario comesout at this stage that many businesses wish to give high level protection tospecific valuable images, which requires high robustness and high visualquality at the same time. Such scenario makes the watermarking schemes shouldbe designed in a "deeper" way which makes the embedding mechanism customized tospecific images. To achieve so, we break the robustness-invisibility trade-offby introducing computation cost in, and propose a novel auto-decoder-likeimage-specified watermarking framework (ISMark). Based on ISMark, the strongrobustness and high visual quality for specific images can be both achieved. Indetail, we apply an optimization procedure (OPT) to replace the traditionalembedding mechanism. Unlike existing schemes that embed watermarks using alearned encoder, OPT regards the cover image as the optimizable parameters tominimize the extraction error of the decoder, thus the features of eachspecified image can be effectively exploited to achieve superior performance.Extensive experiments indicate that ISMark outperforms the state-of-the-artmethods by a large margin, which improves the average bit error rate by 4.64%(from 4.86% to 0.22%) and PSNR by 2.20dB (from 32.50dB to 34.70dB).

Go Wide or Go Deep: Levering Watermarking Performance with Computational Cost for Specific Images