Abstract
Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.
Highlights
Image steganography aims at delivering a modified cover image to secretly transfer hidden information inside with little awareness of the third-party supervision
With the booming trend of convolutional neural networks, a massive amount of neural-network-automated tasks are coming into industrial practices like image auto-labeling through object detection [1,2] and classification [3,4], face recognition [5], pedestrian re-identification [6], etc
We propose a Binary Attention Steganography Network architecture to achieve a relatively high payload capacity (2–3 bpp, bits per pixel) with minimal distortion to other neural-network-automated tasks
Summary
Image steganography aims at delivering a modified cover image to secretly transfer hidden information inside with little awareness of the third-party supervision. Steganalysis algorithms are developed to find out whether an image is embedded with hidden information or not, and, resisting steganalysis detection is one of the major indicators of steganography security. Under such circumstances, a steganography model even with outstanding invisibility to steganalysis methods still cannot be called secure where the spurious label might re-arouse suspicion and, all efforts are made in vain (Source code will be published at: https://github.com/adamcavendish/BASN-LearningSteganography-with-Binary-Attention-Mechanism)
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