SSyncOA: Self-synchronizing Object-aligned Watermarking to Resist Crop-paste Attacks

Chengxin Zhao, Hefei Ling, Sijing Xie, Han Fang, Yaokun Fang, Nan Sun

Abstract: Modern image processing tools can easily crop local objects from images and paste them elsewhere. The challenge posed by this crop-paste attack is that it breaks the synchronization of the image watermark by inducing multiple superimposed desynchronization distortions. Existing image watermarking methods can only resist a single type of desynchronization attack and are inapplicable to this scenario. Finding that the key to resisting the crop-paste attack lies in the geometrically robust features of the object itself, this paper proposes a Self-Synchronizing Object-Aligned watermarking scheme, called SSyncOA. Specifically, we design a self-synchronization process that normalizes the watermark region, the centroid, the principal direction, and the minimum bounding square of the object during encoding and decoding to achieve synchronization of cropping, translation, rotation, and scaling, respectively. In cooperation with SSync, we propose an object-aligned watermarking method that embeds and extracts watermark messages only from the object region. This is achieved by training the watermarking model end-to-end with crop-paste attacks introduced between the encoder and decoder. Extensive experiments illustrate the impact of different desynchronization distortions on the trained watermark model, as well as the superior performance of our method compared to other SOTAs.