3 accepted papers
Existing linguistic steganography methods primarily rely on content transformations to conceal secret messages. However, they often cause subtle yet looking-innocent deviations between normal and stego texts, posing potential security risks in real-world applications. To address this challenge, we p
As Large Language Models (LLMs) integrate into writing workflows, precise governance requires distinguishing ''how AI participated'' rather than merely ''whether AI was used.'' Traditional binary detection often misclassifies ``AI-polished'' content as generated, creating fairness risks. We propose …
AI-generated content has accelerated the topic of media synthesis, particularly Deepfake, which can manipulate our portraits for positive or malicious purposes. Before releasing these threatening face images, one promising forensics solution is the injection of robust watermarks to track their own p…