Are Deepfakes a Game-changer in Digital Images Steganography Leveraging the Cover-Source-Mismatch?

Are Deepfakes a Game-changer in Digital Images Steganography Leveraging the Cover-Source-Mismatch?

연구 분야: Strategies

학회: ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

This work explores the potential of synthetic media generated by the means of Artificial Intelligence (AI), sometimes referred to as Deepfakes, as a source of cover-objects for steganography. Deepfakes offer a vast and diverse pool of media, potentially improving steganographic security by leveraging cover-source mismatch, a challenge in steganalysis where training and testing data come from different sources. The present paper proposes an initial study on Deepfakes’ effectiveness in the field of steganography. More precisely, we propose an initial investigation to assess the impact of Deepfakes on image steganalysis performance in an operational environment. Using a wide range of image generation models and state-of-the-art methods in steganography and steganalysis, we show that Deepfakes can significantly exploit the cover-source mismatch problem but that mitigation solutions also exist. The empirical findings can inform future research on steganographic techniques that exploit cover-source mismatch for enhanced security.