A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
CNN-based Adversarial Embedding for Image Steganography
2019
IEEE Transactions on Information Forensics and Security
Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML)-based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artifacts detectable by other classifiers. In
doi:10.1109/tifs.2019.2891237
fatcat:yqbmvu5qsnf3rfmfvgh4azfz7u