GIFMarking: The robust watermarking for animated GIF based deep learning

Abstract

Animated GIF has become a key communication tool in contemporary social platforms thanks to highly compatible with affective performance, and it is gradually adopted in commercial applications. Therefore, the copyright protection of the animated GIF requires more attention. Digital watermarking is an effective method to embed invisible data into a digital medium that can identify the creator or authorized users. However, few works have been devoted to robust watermarking for the animated GIF. One of the main challenges is that the animated image also contains time frame dimension information compare with still images. This paper proposes a robust blind watermarking framework based 3D convolutional neural networks for the animated GIF image, which achieves watermark image embedding and extraction for the animated GIF. Also, noise simulation is developed in frame-level to ensure robustness for the attack of the temporal dimension in this framework. Furthermore, the invisibility of the watermarked animated image is optimized by adversarial learning. Experimental results provide the effectiveness of the proposed framework and show advantages over existing works.