News Image Steganography: A Novel Architecture Facilitates the Fake News Identification

TL;DR

This paper introduces News Image Steganography (NIS), a GAN-based architecture that encodes source summaries into images to detect inconsistencies in fake news, improving identification accuracy.

Contribution

The novel NIS architecture enables imperceptible encoding of image summaries, facilitating effective detection of tampered or misused images in fake news.

Findings

NIS achieves high accuracy in fake news detection.

Encoded images successfully carry source summaries.

Inconsistencies are effectively revealed using NIS.

Abstract

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.