Traditional steganography methods often hide secret data by establishing a mapping relationship between secret data and a cover image or directly in a noisy area, but has a low embedding capacity. Based on the thought of deep learning, in this paper, we propose a new image steganography scheme based on a U-Net structure. First, in the form of paired training, the trained deep neural network includes a hiding network and an extraction network; then, the sender uses the hiding network to embed the secret image into another full-size image without any modification and sends it to the receiver. Finally, the receiver uses the extraction network to reconstruct the secret image and original cover image correctly. The experimental results show that the proposed scheme compresses and distributes the information of the embedded secret image into all available bits in the cover image, which not only solves the obvious visual cues problem, but also increases the embedding capacity.

Image steganography is a technology that hides sensitive information into an image. The traditional image steganography method tends to securely embed secret information in the host image so that the payload capacity is almost ignored and the steganographic image quality needs to be improved for the Human Visual System(HVS). Therefore, in this work, we propose a new high capacity image steganography method based on deep learning. The Discrete Cosine Transform(DCT) is used to transform the secret image, and then the transformed image is encrypted by Elliptic Curve Cryptography(ECC) to improve the anti-detection property of the obtained image. To improve steganographic capacity, the SegNet Deep Neural Network with a set of Hiding and Extraction networks enables steganography and extraction of full-size images. The experimental results show that the method can effectively allocate each pixel in the image so that the relative capacity of steganography reaches 1. Besides, the image obtained using this steganography method has higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) values, reaching 40dB and 0.96, respectively.

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This paper proposes an efficient scheme for generating image hashing by combining the local texture and color angle features. During the stage of texture extraction, using Weber's Law, the difference ratios between the center pixels and their surrounding pixels are calculated and the dimensions of these values are further reduced by applying principal component analysis to the statistical histogram. In the stage of color feature extraction, the color angle of each pixel is computed before dimensional reduction and is carried out using a discrete cosine transform and a significant coefficients selection strategy. The main contribution of this paper is a novel construction for image hashing that incorporates texture and color features by using Weber local binary pattern and color angular pattern. The experimental results demonstrate the efficacy of the proposed scheme, especially for the perceptual robustness against common contentpreserving manipulations, such as the JPEG compression, Gaussian low-pass filtering, and image scaling. Based on the comparisons with the state-of-the-art schemes, receiver operating characteristic curves and integrated histograms of normalized distances show the superiority of our scheme in terms of robustness and discrimination.INDEX TERMS Image hashing, Weber's law, local binary pattern, color angular pattern.

Aiming at the problem that the traditional steganography based on carrier modification has the low steganographic capacity, a steganographic scheme based on Fully Convolutional Dense Connection Network (FC-DenseNet) is proposed. Since FC-DenseNet can effectively overcome the problems of gradient dissipation and gradient explosion, and a large number of features are multiplexed, the cascaded secret image and carrier image can reconstruct good image quality after entering the network. Effectively improve steganographic capacity. First, we reset the number of input channels of the first convolution block of FC-DenseNet and the number of output channels of the last convolution block and deleted the LogSoftmax() function. On the sender side, after the concatenated secret image and carrier image pass through the hidden network FC-DenseNet, the secret image is embedded in the carrier image to obtain a stego-image. At the receiving side, the extraction network reconstructs the secret image from the stegoimage. Experimental results show that our proposed steganography scheme not only has a high Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity(SSIM) but also can realize large-capacity image steganography, with an average image payload capacity of 23.96 bit per pixel.