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• W3106688601 abstract "Image hiding, one of the most important data hiding techniques, is widely used to enhance cybersecurity when transmitting multimedia data. In recent years, deep learning-based image hiding algorithms have been designed to improve the embedding capacity whilst maintaining sufficient imperceptibility to malicious eavesdroppers. These methods can hide a full-size secret image into a cover image, thus allowing full-image-to-image hiding. However, these methods suffer from a trade-off challenge to balance the possibility of detection from the container image against the recovery quality of secret image. In this paper, we propose Camouflage Generative Adversarial Network (Cam-GAN), a novel two-stage coverless full-image-to-image hiding method named, to tackle this problem. Our method offers a hiding solution through image synthesis to avoid using a modified cover image as the image hiding container and thus enhancing both image hiding imperceptibility and recovery quality of secret images. Our experimental results demonstrate that Cam-GAN outperforms state-of-the-art full-image-to-image hiding algorithms on both aspects." @default.
• W3106688601 created "2020-12-07" @default.
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• W3106688601 date "2020-10-11" @default.
• W3106688601 modified "2023-09-26" @default.
• W3106688601 title "Camouflage Generative Adversarial Network: Coverless Full-image-to-image Hiding" @default.
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• W3106688601 doi "https://doi.org/10.1109/smc42975.2020.9283054" @default.
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