Abstract
The deep-learning steganography of current hotspots can conceal an image secret message in a cover image of the same size. While the steganography secret message is primarily removed via active steganalysis. The document image as the secret message in deep-learning steganography can deliver a considerable amount of effective information in a secret communication process. This study builds and implements deep-learning steganography removal models of document image secret messages based on the idea of adversarial perturbation removal: feed-forward denoising convolutional neural networks (DnCNN) and high-level representation guided denoiser (HGD). Further—considering the large computation cost and storage overheads of the above model—we use the document image-quality assessment (DIQA) as threshold, calculate the importance of filters using geometric median and prune redundant filters as extensively as possible through the overall iterative pruning and artificial bee colony (ABC) automatic pruning algorithms to reduce the size of the network structure of the existing vast and over-parameterized deep-learning steganography removal model, while maintaining the good removal effects of the model in the pruning process. Experiment results showed that the model generated by this method has better adaptability and scalability. Compared with the original deep-learning steganography removal model without pruning in this paper, the classic indicators params and flops are reduced by more than 75%.
Highlights
Image steganography is a technique for concealing secret messages in cover images and transmitting stego images to complete transmission of secret messages in a common channel.The receiving end of the transmission can leak the secret message
Baluja [5] proposed a convolutional neural networks (CNNs) based on the encoder–decoder structure, the encoder network can successfully conceal a secret image into a same-size cover image and the decoder network can reveal the secret image completely
Iterative pruning is the maximum pruning of each layer under the conditions of structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and document image-quality assessment (DIQA) threshold
Summary
Image steganography is a technique for concealing secret messages in cover images and transmitting stego images to complete transmission of secret messages in a common channel.The receiving end of the transmission can leak the secret message. Image steganography is a technique for concealing secret messages in cover images and transmitting stego images to complete transmission of secret messages in a common channel. Deep-learning steganography has progressed considerably with larger payloads in secret messages than the traditional steganography and successfully distributes secret messages to available bits of the cover image. Lossy deep steganography limits secret messages to images. Baluja [5] proposed a CNN based on the encoder–decoder structure, the encoder network can successfully conceal a secret image into a same-size cover image and the decoder network can reveal the secret image completely. Wu et al [6] put forward a deep-learning steganography based on CNN architecture to provide better payload and performance compared with the traditional steganography method. Dong et al [7] offered a deep-learning steganography called ISGAN by Symmetry 2020, 12, 1426; doi:10.3390/sym12091426 www.mdpi.com/journal/symmetry
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