# SWIFT: Semantic Watermarking for Image Forgery Thwarting

arXiv: https://arxiv.org/abs/2407.18995 · HTML: https://mlsanity.com/paper/2407.18995/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Emu Edit: Precise Image Editing via Recognition and Generation Tasks](https://mlsanity.com/paper/2311.10089/) (2023)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Spreading vectors for similarity search](https://mlsanity.com/paper/1806.03198/) (2018)
- [Image Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2204.08610/) (2022)
- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)
- [Compression Represents Intelligence Linearly](https://mlsanity.com/paper/2404.09937/) (2024)
- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [LLMZip: Lossless Text Compression using Large Language Models](https://mlsanity.com/paper/2306.04050/) (2023)

## Built on by (most cited first, 8 of 8)

- [Watermark Anything with Localized Messages](https://mlsanity.com/paper/2411.07231/) (2024)
- [SEAL: Semantic Aware Image Watermarking](https://mlsanity.com/paper/2503.12172/) (2025)
- [Fast, Secure, and High-Capacity Image Watermarking with Autoencoded Text Vectors](https://mlsanity.com/paper/2510.00799/) (2025)
- [MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks](https://mlsanity.com/paper/2509.10766/) (2025)
- [The Forensic Cost of Watermark Removal: From Dedicated Attacks to Image Editing](https://mlsanity.com/paper/2604.25491/) (2026)
- [Watermarking Large Language Models in Europe: Interpreting the AI Act in Light of Technology](https://mlsanity.com/paper/2511.03641/) (2025)
- [DeepSignature: Digitally Signed, Content-Encoding Watermarks for Robust and Transparent Image Authentication](https://mlsanity.com/paper/2604.23016/) (2026)
- [Semantic Watermarking for Malicious Image Manipulation Detection](https://mlsanity.com/paper/2609.39623/) (2026)

## Abstract (arXiv)

This paper proposes a novel approach towards image authentication and tampering detection by using watermarking as a communication channel for semantic information. We modify the HiDDeN deep-learning watermarking architecture to embed and extract high-dimensional real vectors representing image captions. Our method improves significantly robustness on both malign and benign edits. We also introduce a local confidence metric correlated with Message Recovery Rate, enhancing the method's practical applicability. This approach bridges the gap between traditional watermarking and passive forensic methods, offering a robust solution for image integrity verification.