A biological image classification method based on improved CNN

Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security.

Secure Robust JPEG Steganography Based on AutoEncoder With Adaptive BCH Encoding

A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition

Most of the existing coverless steganography approaches have poor robustness to geometric attacks, because these approaches use features of the entire image to map information, and these features are easy to be lost when being attacked. In order to improve the robustness against geometric attacks, we propose a coverless image steganography method based on multi-object recognition. In this scheme, we firstly use Faster RCNN to detect objects in the image data set, establish a mapping dictionary between object labels and binary sequence. Then we propose a novel mapping rule based on the filtered robust object labels for sequence generation. Therefore, an image can generate robust binary sequence through multi-objects recognition. In the transmission process, the transmitted image has not been modified, so our method can fundamentally resist steganalysis tools and avoid the attacker’s suspicions. In addition, the capacity and hiding rate of the proposed method are both considerable. Evaluations with under geometric attacks shows, on average, $3.1\times $ robustness increase over other five coverless steganography methods. Moreover, evaluations under ten noise attacks shows, on average, the robustness of our method is also excellent, which reaches 83%.

Coverless Image Steganography Based on Multi-Object Recognition

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

https://www.mdpi.com/2227-7390/8/9/1394/pdf

Coverless Image Steganography Based on Generative Adversarial Network

Recently, researches have shown that coverless image steganography can resist the existing steganalysis tools effectively. On this basis, a novel coverless image steganography algorithm based on image retrieval of DenseNet features and DWT sequence mapping is proposed in this paper. Firstly, DenseNet convolutional neural network model in deep learning is used to extract the features of image datasets. Supervised learning is adopted to retrieve the image, and the retrieval results can be used as the information carrier. Secondly, the selected images are divided into 4 × 4 sub-blocks for block discrete wavelet transform. Then the D W T coefficient are calculated based on the low-frequency components after block transformation, and the coefficients between blocks are scanned according to the Zigzag scan, such that the robust feature sequences are generated. Finally, the secret information is divided into segments with the same length as the feature sequence, and an inverted index is established with feature sequence, the position of blocks, D W T coefficient and image path. The image with the same feature sequence as the secret information segment is selected through index as the carriers. The experimental results and analysis show that this method has better robust and security performance resisting most image attacks compared with the state-of-the-art methods.

Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping

https://www.techscience.com/cmc/v62n2/38287/pdf

Junshan Tan

Papers

Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping

An Adaptive Image Calibration Algorithm for Steganalysis