End-to-end watermarking system
    2.
    发明授权

    公开(公告)号:US12238322B2

    公开(公告)日:2025-02-25

    申请号:US18008789

    申请日:2022-01-11

    Applicant: GOOGLE LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder that generates a watermark and a decoder that decodes a data item encoded within the watermark. The training comprises obtaining a plurality of training images and data items. For each training image, a first watermark is generated using an encoder and a subsequent second watermark is generated by tiling two or more first watermarks. The training image is watermarked using the second watermark to generate a first error value and distortions are added to the watermarked image. A distortion detector predicts the distortions based on which the distorted image is modified. The modified image is decoded by the decoder to generate a predicted data item and a second error value. The training parameters of the encoder and decoder are adjusted based on the first and the second error value.

    END-TO-END WATERMARKING SYSTEM
    3.
    发明公开

    公开(公告)号:US20230362399A1

    公开(公告)日:2023-11-09

    申请号:US18008789

    申请日:2022-01-11

    Applicant: GOOGLE LLC

    CPC classification number: H04N19/467 G06T1/0021

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder that generates a watermark and a decoder that decodes a data item encoded within the watermark. The training comprises obtaining a plurality of training images and data items. For each training image, a first watermark is generated using an encoder and a subsequent second watermark is generated by tiling two or more first watermarks. The training image is watermarked using the second watermark to generate a first error value and distortions are added to the watermarked image. A distortion detector predicts the distortions based on which the distorted image is modified. The modified image is decoded by the decoder to generate a predicted data item and a second error value. The training parameters of the encoder and decoder are adjusted based on the first and the second error value.

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