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首页> 外文期刊>Journal of visual communication & image representation >GIFMarking: The robust watermarking for animated GIF based deep learning
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GIFMarking: The robust watermarking for animated GIF based deep learning

机译：GIFMARKING：基于动画GIF的深度学习的强大水印

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摘要

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.
机译：动画GIF已成为当代社交平台中的关键通信工具，由于与情感性能高度兼容，并且在商业应用中逐步采用。因此，动画GIF的版权保护需要更多关注。数字水印是一种有效的方法，可以将不可见数据嵌入到可以识别创建者或授权用户的数字介质中。然而，对于动画GIF，很少有效地致力于强大的水印。主要挑战之一是动画图像还包含与静止图像比较的时间帧维度信息。本文提出了一种用于动画GIF图像的基于基于3D卷积神经网络的坚固般的盲目性神经网络，其实现了用于动画GIF的水印图像嵌入和提取。此外，在帧级中开发了噪声仿真，以确保在该框架中攻击时间维度的稳健性。此外，通过对抗性学习优化了水印的动画图像的隐形。实验结果提供了拟议的框架的有效性，并显示出对现有工作的优势。

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