In traditional steganographic schemes, RGB three channels payloads are assigned equally in a true color image. In fact, the security of color image steganography relates not only to data-embedding algorithms but also to different payload partition. How to exploit inter-channel correlations to allocate payload for performance enhancement is still an open issue in color image steganography. In this paper, a novel channel-dependent payload partition strategy based on amplifying channel modification probabilities is proposed, so as to adaptively assign the embedding capacity among RGB channels. The modification probabilities of three corresponding pixels in RGB channels are simultaneously increased, and thus the embedding impacts could be clustered, in order to improve the empirical steganographic security against the channel co-occurrences detection. The experimental results show that the new color image steganographic schemes, incorporated with the proposed strategy, can effectively make the embedding changes concentrated mainly in textured regions, and achieve better performance on resisting the modern color image steganalysis.

A New Payload Partition Strategy in Color Image Steganography

Successful adaptive steganography has mainly focused on embedding the payload while minimizing an appropriately defined distortion function. The application of deep learning to steganalysis has greatly challenged present adaptive steganographic methods, but has also shown the potential for the improvement of steganography. This paper proposes a distortion function generating a framework for steganography. It has three modules: a generator with a U-Net architecture to translate a cover image into an embedding change probability map, a no-pre-training-required double-tanh function to approximate the optimal embedding simulator while preserving gradient norm during backpropagation in the adversarial training, and an enhanced steganalyzer based on a convolution neural network together with multiple high pass filters as the discriminator. Extensive experimental results on different datasets have shown that the proposed framework outperforms the current state-of-the-art steganographic schemes. Moreover, the adversarial training time is reduced dramatically compared with the GAN-based automatic steganographic distortion learning framework (ASDL-GAN).

An Embedding Cost Learning Framework Using GAN

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.

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Reversible Image Steganography Scheme Based on a U-Net Structure

In traditional information hiding methods, the secret information is embedded into the carriers, which will inevitably leave traces of modification on the carriers In those methods, the modified images can be easily detected by some steganalysis algorithm and thus the security can not be guaranteed To address this problem, the concept of coverless information hiding is proposed However, general coverless information hiding method has a lower information hiding capacity In this paper, we propose a novel coverless information hiding method based on the average pixel values of sub-images We generate hash sequences by a hashing algorithm and realize the secret information hiding through mapping relationship In the first place, we build a dictionary and a hash array Then we map the dictionary and the hash array through mapping relationship Furthermore, we build a multi-level index structure for retrieving the stego-images efficiently The experimental results and analysis show that our method has a good performance in the capacity of information hiding, the security, the robustness to image attacks and the hiding success rate based on different image databases

A novel coverless information hiding method based on the average pixel value of the sub-images

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods.

Concealed Attack for Robust Watermarking Based on Generative Model and Perceptual Loss

Steganography detection based on Rich Model features is a hot research direction in steganalysis. However, rich model features usually result a large computation cost. To reduce the dimension of steganalysis features and improve the efficiency of steganalysis algorithm, differing from previous works that normally proposed new feature extraction algorithm, this paper proposes a general steganalysis feature selection method based on decision rough set $\alpha$ -positive region reduction. First, it is pointed out that decision rough set $\alpha$ -positive region reduction is suitable for steganalysis feature selection. Second, a quantization method of attribute separability is proposed to measure the separability of steganalysis feature components. Third, steganalysis feature components selection algorithm based on decision rough set $\alpha$ -positive region reduction is given; thus, stego images can be detected by the selected feature. The proposed method can significantly reduce the feature dimensions and maintain detection accuracy. Based on the BOSSbase-1.01 image database of 10 000 images, a series of feature selection experiments are carried on two kinds of typical rich model features (35263-D J+SRM feature and 17000-D GFR feature). The results show that even though these two kinds of features are reduced to approximately 8000-D, the detection performance of steganalysis algorithms based on the selected features are also maintained with that of original features, which will remarkably improve the efficiency of feature extraction and stego image detection.

Selection of Rich Model Steganalysis Features Based on Decision Rough Set $\alpha$ -Positive Region Reduction

Considering that traditional image steganography technologies suffer from the potential risk of failure under lossy channels, an enhanced adaptive steganography with multiple robustness against image processing attacks is proposed, while maintaining good detection resistance. First, a robust domain constructing method is proposed utilizing robust element extraction and optimal element modification, which can be applied to both spatial and JPEG images. Then, a robust steganography is proposed based on “Robust Domain Constructing + RS-STC Codes,” combined with cover selection, robust cover extraction, message coding, and embedding with minimized costs. In addition, to provide a theoretical basis for message extraction integrity, the fault tolerance of the proposed algorithm is deduced using error model based on burst errors and decoding damage. Finally, on the basis of parameter discussion about robust domain construction, performance experiments are conducted, and the recommended coding parameters are given for lossy channels with different attacks using the analytic results for fault tolerance. A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, such as compression, noising, scaling and other attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance.

Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels

Robust JPEG steganographic algorithms are proposed to protect the embedded message when the covert JPEG image is JPEG-compressed in some lossy channel. They usually perform much better on anti-compression ability than the traditional adaptive JPEG steganographic algorithms. However, massive recent experimental results reveal that the message extraction accuracy of the present robust JPEG steganographic methods are not high enough in some JPEG compressing channels. Thus, this paper proposes a new robust JPEG steganographic algorithm with high message extraction accuracy with equivalent detection resistance. First, the robust channel for the JPEG compression and steganographic embedding are analyzed. Second, a new error correction encoder that can protect the message in lossy channel is proposed. The structure and inside codes of the proposed encoder are different to the traditional codes used in adaptive steganography. Last, the proposed code, the relationship between coefficients and the minimal distortion model are combined to build new steganography. 10,000 images in the popular BOSSbase 1.01 image library are selected for magnanimous experiments. Compared with the adaptive JPEG steganographic algorithm and some current robust JPEG steganographic algorithms, the experimental results show that proposed method can not only resist detection efficiently, but also obtain higher extracting accuracy on resisting JPEG compression.

Yi Zhang

Papers

Selection of Rich Model Steganalysis Features Based on Decision Rough Set $\alpha$ -Positive Region Reduction

Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels

A robust image steganography based on the concatenated error correction encoder and discrete cosine transform coefficients