SWIFT: Semantic Watermarking for Image Forgery Thwarting

Gautier Evennou, Vivien Chappelier, Ewa Kijak, Teddy Furon

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.

External IDs:dblp:conf/wifs/EvennouCKF24