As a large number of images are transmitted through social networks every moment, terrorists may hide data into images to convey secret data. Various types of images are mixed up in the social networks, and it is difficult for the servers of social networks to detect whether the images are clean. To prevent the illegal communication, this paper proposes a method of defeating data hiding by removing the secret data without impacting the original media content. The method separates the clean images from illegal images using the generative adversarial network (GAN), in which a deep residual network is used as a generator. Therefore, hidden data can be removed and the quality of the processed images can be well maintained. Experimental results show that the proposed method can prevent secret transmission effectively and preserve the processed images with high quality.

中文翻译：

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使用生成对抗网络击败社交网络中的数据隐藏

由于每时每刻都通过社交网络传输大量图像，恐怖分子可能会将数据隐藏在图像中以传达秘密数据。社交网络中混合了各种类型的图像，并且社交网络的服务器难以检测图像是否干净。为了防止非法通信，本文提出了一种在不影响原始媒体内容的情况下，通过删除秘密数据来消除数据隐藏的方法。该方法使用生成对抗网络（GAN）将干净的图像与非法图像分离，其中使用深度残差网络作为生成器。因此，可以去除隐藏的数据，并且可以很好地保持处理后的图像的质量。