Work

SWIFT: Semantic Watermarking for Image Forgery Thwarting

Year: 2024

Type: conference-paper

Abstract: 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. The code is available at https://github.com/gautierevn/swift_watermarking.

Source: IEEE International Workshop on Information Forensics and Security

DOI: 10.1109/wifs61860.2024.10810692

Authors: Gautier Evennou, Vivien Chappelier, Ewa Kijak, Teddy Furon

Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, ARTISHAU: Intelligence artificielle : sécurité, traçabilité et audit, Université de Rennes

Language: English

Topic: Advanced Steganography and Watermarking Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Security of AI for Defense Applications