# Traceable and Authenticable Image Tagging for Fake News Detection

arXiv: https://arxiv.org/abs/2211.10923 · HTML: https://mlsanity.com/paper/2211.10923/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [StegaStamp: Invisible Hyperlinks in Physical Photographs](https://mlsanity.com/paper/1904.05343/) (2019)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [Exploiting Multi-domain Visual Information for Fake News Detection](https://mlsanity.com/paper/1908.04472/) (2019)
- [Proactive Image Manipulation Detection](https://mlsanity.com/paper/2203.15880/) (2022)
- [FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes](https://mlsanity.com/paper/2204.01960/) (2022)
- [Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification](https://mlsanity.com/paper/1910.12203/) (2019)
- [VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text](https://mlsanity.com/paper/2102.00816/) (2021)
- [A Compact Neural Network-based Algorithm for Robust Image Watermarking](https://mlsanity.com/paper/2112.13491/) (2021)
- [NewsStories: Illustrating articles with visual summaries](https://mlsanity.com/paper/2207.13061/) (2022)

## Built on by (most cited first, 4 of 4)

- [Robust Identity Perceptual Watermark Against Deepfake Face Swapping](https://mlsanity.com/paper/2311.01357/) (2023)
- [Semantic Deep Hiding for Robust Unlearnable Examples](https://mlsanity.com/paper/2406.17349/) (2024)
- [Proactive Schemes: A Survey of Adversarial Attacks for Social Good](https://mlsanity.com/paper/2409.16491/) (2024)
- [GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media](https://mlsanity.com/paper/2310.07668/) (2023)

## Abstract (arXiv)

To prevent fake news images from misleading the public, it is desirable not only to verify the authenticity of news images but also to trace the source of fake news, so as to provide a complete forensic chain for reliable fake news detection. To simultaneously achieve the goals of authenticity verification and source tracing, we propose a traceable and authenticable image tagging approach that is based on a design of Decoupled Invertible Neural Network (DINN). The designed DINN can simultaneously embed the dual-tags, textiti.e., authenticable tag and traceable tag, into each news image before publishing, and then separately extract them for authenticity verification and source tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a parallel Feature Aware Projection Model (FAPM) to help the DINN preserve essential tag information. In addition, we define a Distance Metric-Guided Module (DMGM) that learns asymmetric one-class representations to enable the dual-tags to achieve different robustness performances under malicious manipulations. Extensive experiments, on diverse datasets and unseen manipulations, demonstrate that the proposed tagging approach achieves excellent performance in the aspects of both authenticity verification and source tracing for reliable fake news detection and outperforms the prior works.