A larger portion of fake news quotes untampered images from other sources with ulterior motives rather than conducting image forgery. Such elaborate engraftments keep the inconsistency between images and text reports stealthy, thereby, palm off the spurious for the genuine. This paper proposes an architecture named News Image Steganography (NIS) to reveal the aforementioned inconsistency through image steganography based on GAN. Extractive summarization about a news image is generated based on its source texts, and a learned steganographic algorithm encodes and decodes the summarization of the image in a manner that approaches perceptual invisibility. Once an encoded image is quoted, its source summarization can be decoded and further presented as the ground truth to verify the quoting news. The pairwise encoder and decoder endow images of the capability to carry along their imperceptible summarization. Our NIS reveals the underlying inconsistency, thereby, according to our experiments and investigations, contributes to the identification accuracy of fake news that engrafts untampered images.

中文翻译：

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新闻图像隐写术：一种新颖的体系结构有助于伪造新闻识别

大部分虚假新闻引用别有用心的其他来源的未经篡改的图像，而不是进行图像伪造。如此精心的植入使图像和文本报告之间的不一致隐身了，从而摆脱了真正的伪造。本文提出了一种称为新闻图像隐写术（NIS）的体系结构，以通过基于GAN的图像隐写术揭示上述不一致之处。基于新闻图像的源文本生成关于新闻图像的提取摘要，并且学习的隐写算法以接近感知隐身的方式对图像的摘要进行编码和解码。一旦引用了编码的图像，就可以对其源摘要进行解码，并进一步将其作为地面真实性来表示，以验证引用的新闻。成对的编码器和解码器赋予图像进行其不可察觉的汇总的能力。我们的NIS揭示了潜在的不一致性，因此根据我们的实验和调查，这有助于识别植入未经篡改图像的假新闻的准确性。