In deep steganography, the model size is usually related to the grid resolution of the underlying layer, and a separate neural network needs to be trained as a message extractor. We propose image steganography based on generative implicit neural representation, which breaks through the limitation of image resolution using a continuous function to represent image data and allows various kinds of multimedia data to be used as the cover image for steganography, which theoretically extends the class of carriers. Fixing a neural network as a message extractor, and transferring the training of the network to the training of the image itself, reduces the training cost and avoids the problem of exposing the steganographic behavior caused by the transmission of the message extractor. The experiment proves that the scheme is efficient, and it only takes 3 s to complete the optimization for an image with a resolution of 64×64 and a hiding capacity of 1 bpp, and the accuracy of message extraction reaches 100%.
Multi-image hiding, which embeds multiple secret images into a cover image and is able to recover these images with high quality, has gradually become a research hotspot in the field of image steganography. However, due to the need to embed a large amount of data in a limited cover image space, issues such as contour shadowing or color distortion often arise, posing significant challenges for multi-image hiding. We propose StegaINR4MIH, a implicit neural representation steganography framework that enables the hiding of multiple images within a single implicit representation function. In contrast to traditional methods that use multiple encoders to achieve multi-image embedding, our approach leverages the redundancy of implicit representation function parameters and employs magnitude-based weight selection and secret weight substitution on pre-trained cover image functions to effectively hide and independently extract multiple secret images. We conduct experiments on images with a resolution from three different datasets: CelebA-HQ, COCO, and DIV2K. When hiding two secret images, the PSNR values of both the secret images and the stego images exceed 42. When hiding five secret images, the PSNR values of both the secret images and the stego images exceed 39. Extensive experiments demonstrate the superior performance of the proposed method in terms of visual quality and undetectability.
Deep learning technology has developed rapidly in recent years, and deep learning-based steganography and steganalysis techniques have also achieved fruitful results. In the past few years, the over-expanded structure of steganalyzers based on deep learning has led to huge computational and storage costs. In this article, we propose image steganalysis based on model compression, and apply the model compression method to image steganalysis to reduce the network infrastructure of the existing large-scale over-parameter steganalyzer based on deep learning. We conducted extensive experiments on the BOSSBase+BOWS2 dataset. As can be seen from the experiment, compared with the original steganalysis model, the model structure we proposed can achieve performance with fewer parameters and floating-point operations. This model has better portability and scalability.
To achieve a high embedding capacity (EC) without any distortion in the directly decrypted result, a reversible data hiding in encrypted images (RDH-EI) scheme based on full bit-plane compression (FBPC) is proposed. FBPC is designed to vacate as much room as possible before image encryption. To enrich the adjacent redundancy within the most significant bit (MSB) planes, we use flip prediction on the first MSB and then replace the other MSB planes with the XOR result between that plane and its higher level one successively. Hilbert curve scanning is introduced to reduce the dimensionality of the planes to obtain eight highly redundant bit sequences. Huffman coding is then utilizing to compress the bit sequences. After FBPC, a compressed image is obtained. Stream cipher with self-feedback is adapted to encrypt the image. Additional data could be embedded into the reserved space of the encrypted domain. Compared with existing RDH-EI algorithms, the proposed scheme could have a higher EC with no distortion in the directly decrypted image due to the high compression performance of FBPC. Experimental results on the BOWS-2 and BOSSbass datasets demonstrate that the average EC can reach 3.56 and 3.71 bits per pixel, respectively.
With the aim of reducing the distortion of the marked image to realize high-fidelity reversible data hiding (RDH), we propose an RDH scheme based on dynamic prediction and expansion (DPE). By introducing a dynamic pixels-value-ordering (D-PVO) method into DPE, a full reversibility can be achieved. The pixels of the cover image are predicted by sorting their cross-over neighbors and using the two end pixels or the median pixels to conduct the prediction. The covert data are embedded by using prediction-error-expansion. We have theoretically proved higher prediction accuracy for the proposed method compared with the two existing classical prediction methods. In our experiments, we first verify the prediction accuracy of D-PVO by applying it to Gaussian distributed sampled data. Next, we perform experiments on the standard pictures from USC-SIPI and Kodak image datasets. The experimental results demonstrate that the proposed scheme can ensure a full recovery of the original image and a higher fidelity of the marked cover images, and the peak signal-to-noise ratio can exceed 60.00 dB when embedding capacity reaches 10,000 bits.
With the aim of reducing the distortion of the marked image to realize high-fidelity reversible data hiding (RDH), we propose an RDH scheme based on dynamic prediction and expansion (DPE). By introducing a dynamic pixels-value-ordering (D-PVO) method into DPE, a full reversibility can be achieved. The pixels of the cover image are predicted by sorting their cross-over neighbors and using the two end pixels or the median pixels to conduct the prediction. The covert data are embedded by using prediction-error-expansion. We have theoretically proved higher prediction accuracy for the proposed method compared with the two existing classical prediction methods. In our experiments, we first verify the prediction accuracy of D-PVO by applying it to Gaussian distributed sampled data. Next, we perform experiments on the standard pictures from USC-SIPI and Kodak image datasets. The experimental results demonstrate that the proposed scheme can ensure a full recovery of the original image and a higher fidelity of the marked cover images, and the peak signal-to-noise ratio can exceed 60.00 dB when embedding capacity reaches 10,000 bits.
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