Coverless image steganography based on DenseNet feature mapping

Liu, Qiang; Xiang, Xuyu; Qin, Jiaohua; Tan, Yun; Qiu, Yao

doi:10.1186/s13640-020-00521-7

Cited by 26 publications

(13 citation statements)

References 34 publications

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“…[18] utilises DWT-based features as image hashes and further designs a retrieval mechanism based on DenseNet to ensure the similarity of candidate images, thus reducing the possibility of detection from manual inspection. To further enhance robustness against geometric attacks, [17,20,21] extract DenseNet features and object labels recognised by Faster-RCNN for secret information mapping.…”

Section: Steganography Without Embeddingmentioning

confidence: 99%

Image Disentanglement Autoencoder for Steganography without Embedding

Liu

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Conventional steganography approaches embed a secret message into a carrier for concealed communication but are prone to attack by recent advanced steganalysis tools. In this paper, we propose Image DisEntanglement Autoencoder for Steganography (IDEAS) as a novel steganography without embedding (SWE) technique. Instead of directly embedding the secret message into a carrier image, our approach hides it by transforming it into a synthesised image, and is thus fundamentally immune to typical steganalysis attacks. By disentangling an image into two representations for structure and texture, we exploit the stability of structure representation to improve secret message extraction while increasing synthesis diversity via randomising texture representations to enhance steganography security. In addition, we design an adaptive mapping mechanism to further enhance the diversity of synthesised images when ensuring different required extraction levels. Experimental results convincingly demonstrate IDEAS to achieve superior performance in terms of enhanced security, reliable secret message extraction and flexible adaptation for different extraction levels, compared to state-of-the-art SWE methods.

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Section: Steganography Without Embeddingmentioning

confidence: 99%

Image Disentanglement Autoencoder for Steganography without Embedding

Liu

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Dense Block Network. Although a deeper network allows the extraction of deeper semantic information, there will be an inevitable increase in parameters with the deepening of a network [28,29]. As a result, a series of problems are brought to the network optimization and the experimental hardware.…”

Section: Improved Faster R-cnnmentioning

confidence: 99%

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Feng

Tao

Lee

2021

Mathematical Problems in Engineering

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In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

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“…Since the Faster R-CNN algorithm has high robustness in target recognition, the algorithm is also robust and can resist a variety of attacks. Liu et al [28] use feature extractor of DenseNet [29] to map the image into features and then transform the image into secret information. e above four algorithms directly map the secret information to the generated image or use the feature extraction algorithm to extract the secret information from the stego image.…”

Section: Image Steganography Algorithm Based On Nomentioning

confidence: 99%

High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Yang

Wang

et al. 2021

Security and Communication Networks

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Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

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Coverless image steganography based on DenseNet feature mapping

Cited by 26 publications

References 34 publications

Image Disentanglement Autoencoder for Steganography without Embedding

Image Disentanglement Autoencoder for Steganography without Embedding

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

High-Capacity Image Steganography Algorithm Based on Image Style Transfer

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