Image steganography has emerged as a promising channel for transmitting task assignments, authentication credentials, and scheduling metadata across distributed cloud nodes without raising suspicion. However, existing steganography methods primarily focus on robustness against attacks while overlooking the credibility of the extracted information. In distributed cloud environments, blindly accepting incorrect or manipulated data can propagate faults across nodes, trigger inconsistent system states, or even cause cascading service failures. To address this, we propose C2IS, a Credibility-Aware Coverless Image Steganography framework designed for distributed cloud services. C2IS enables cloud nodes not only to extract hidden information, but also to assess its credibility before execution or propagation. Specifically, we propose a polar harmonic transform-based feature extraction and selection strategy that extracts both coarse-grained and fine-grained features. The coarse-grained features, characterized by their high stability under various attacks, are utilized to carry secret information. Meanwhile, the fine-grained features quantify the degree of feature perturbation to assess information credibility. Furthermore, we develop a double median thresholding-based hash mapping algorithm, which binarizes inter-image feature differences using the median, thereby significantly enhancing the diversity of the generated hash sequences. Extensive experimental results show that our method achieves a complete hash sequence length of up to 13 bits, enabling higher hiding capacity. Meanwhile, under various attacks, it exhibits stronger robustness than state-of-the-art methods and can accurately determine whether the extracted information contains errors, providing a trustworthy means of covert communication for distributed cloud services.

Deep learning-based watermarking technology has made significant success due to its excellent robustness and convenient traceability. However, previous studies rarely considered users’ demands for high-fidelity images and file size growth rates, especially in scenarios involving the watermarking of a large number of high-resolution images. This paper proposes a high fidelity and robust watermarking using deep reinforcement learning. Specifically, we propose a self-optimization module that utilizes a dueling Q-learning network to select the optimal watermark embedding patch, which minimizes the quality impact after adding a watermark to the image. Additionally, we incorporate a convolutional block attention module (CBAM) into encoder and decoder networks to enhance feature learning in both frequency and spatial dimensions. Experimental results demonstrate that our method significantly enhances image fidelity, achieving PSNR values above 54dB across different datasets. The method also exhibits good robustness against common image manipulations, including both non-geometric and geometric attacks. Furthermore, our method achieves an FSVR value below 0.5, reducing file size inflation by a factor of 34 compared to existing advanced methods. This makes it very friendly for storing a large number of high-resolution watermarked images.

In reversible data hiding (RDH) in the plaintext domain, the reversibility of the data and the image is the greatest strength but also comes with limitations, such as low embedding capacity and weak generalization ability. These limitations make it challenging for RDH to be applied in scenarios that require the concealment of high-capacity data. To address these issues, we propose a compatible reversible data hiding with high capacity and generalization (CRDH), which can perform a second embedding based on all existing RDH methods and the two extractions are independent of each other. The nearest-neighbor interpolation (NNI) algorithm and integer wavelet transform are initially designed to create additional redundancy room, diverging from existing RDH methods that typically exploit the inherent redundancy within the image itself. Following this, we derive a novel method to prevent pixel value overflow or underflow, which is employed to guide the data embedding process. In the experimental results on standard test images, the average maximum embedding capacity of the CRDH method reaches 4.41 bits per pixel (BPP), which is 1.98 times that of other methods. As the embedded data increases, the peak signal-to-noise ratio (PSNR) of CRDH’s stego-images becomes higher compared to other methods. Furthermore, CRDH exhibits a significantly superior generalization ability in terms of both capacity and quality compared to state-of-the-art RDH methods.

To avoid the pixel modification traces left by steganography from being detected by passive steganalysis, and to prevent the hidden data from being destroyed by active steganalysis attacks, Coverless Image Steganography (CIS) that does not modify pixels has attracted widespread attention. However, most existing CIS methods are limited in their maximum capacity due to insufficient diversity in their hash sequences. In addition, these methods struggle to maintain high robustness against both geometric and non-geometric attacks simultaneously. To address these two issues, a new coverless image steganography method is proposed to enhance CIS methods’ applicability, security, and robustness in highly insecure networks. During the hiding process, hash sequences are generated by a SHA-256 algorithm that integrates inter-block and inter-channel fusion, providing higher diversity than other CIS methods. Consequently, the proposed CIS method achieves higher capacity on publicly available datasets. During the extraction process, an evaluation metric that combines visual and histogram similarity is designed to improve the accuracy of inverse image retrieval. The experimental results demonstrate that the proposed CIS method achieves capacity increases of 26.73% and 38.34% over other CIS methods on the VOC and COCO datasets, respectively. Moreover, this method exhibits nearly 100% robustness against common active steganalysis.

Orthogonal Moment-based Robust Reversible Watermarking (OM-RRW) is crucial for intellectual property protection, providing the dual benefits of robustness and reversibility. However, OM-RRW embeds watermarks into visually sensitive global low-frequency features, which easily leads to ring-like distortions that expose watermark locations, making them vulnerable to removal through image inpainting. To address this issue, this paper makes the first attempt to introduce an innovative strategy to eliminate these visible distortions, thereby overcoming OM-RRW's inherent limitations. The strategy innovates on two fronts: first, it customizes varying embedding step sizes based on the stability differences of moment values to minimize distortion; second, it designs a texture-aware adaptive basis function fine-tuning strategy. This strategy adjusts the representation capability of the basis functions in different regions based on the human eye's sensitivity to various texture areas, helping to avoid visible ring-like distortions. The performance of the proposed method is evaluated using Polar Harmonic Transform (PHT) moments, comprising three moments that exhibit remarkable performance in existing OM-RRW methods. Extensive experiments show that the proposed method can embed 128-bit watermarks with no visible distortions while minimizing the loss of robustness. In addition, this paper finds that OM-RRW demonstrates satisfactory robustness against VAE watermark removal attacks.

Medical images play a crucial role in doctors' clinical diagnosis and treatment. However, the transmission and sharing of such private information raises security concerns. To address this issue, image hiding is used as an effective technique to protect images. To achieve large hiding capacity, lossless recovery and anti-steganalysis, we propose a novel two-stage medical image-hiding method in this paper. In the first stage, a QR code for the patient diagnosis information (PDI) is generated and embedded into a secret medical image using reversible data hiding. In the second stage, the secret medical image containing PDI is hidden in a natural target image. A kind of lossless compression technique named soft compression is innovatively introduced in two hiding stages, to ensure that the reconstructed secret medical image and PDI are exactly identical to the original ones. Moreover, an adaptiven-LSB model is proposed to improve the stego image quality. Extensive experimental results show that our method achieves a PSNR of over 40dB for the stego image at 2 BPP while recovering the PDI and secret medical image with 100% accuracy on the DIV2K, COCO and ImageNet datasets. It outperforms other state-of-the-art methods in terms of hiding invisibility, recovery accuracy and security.

The goal of multiple-image hiding is to hide several secret images within another carrier image without significantly changing its appearance, and then perfectly reconstruct all of the secret images. The challenge is to ensure that the stego-image has great visual quality and can resist various steganalysis under the premise of hiding as much information as possible in one image. To address this issue, the majority of known image-hiding methods focus on hiding images using compression techniques. In this article, we present a novel multiple-image hiding method based on up-sampling and reversible color transformation. First, the interpolation algorithm up-samples the carrier image, so that the attribute of similar neighboring pixel values in the up-sampled image can significantly improve the effect of image hiding. The embedding procedure is then performed using the proposed Euclidean Distance (ED)-based block matching and reversible color transformation, which decreases the chance of local blurring in the stego-image. Experimental results show that the proposed method surpasses existing advanced methods by achieving an average of 33 dB and 28 dB of PSNR for the stego-image with a hiding capacity 2 BPP and 8 BPP, and obtaining 100% reconstructing accuracy for all secret images. It also has a high level of resistance to steganalysis and a strong robustness against various image-processing attacks.

In the field of image steganography, quality, security, and capacity emerge as three crucial aspects for ensuring the security of images stored on cloud servers. However, many existing methods fail to strike a balance on these three aspects according to the various requirements of image users. To solve this issue, we propose an Adaptable Image Steganography Model (AISM) capable of customizing suitable steganography strategies for different user requirements. Initially, AISM customizes appropriate down-sampling methods, ratios, and up-sampling ratios for secret/cover images based on requirements, followed by the image sampling process. Following that, an embedding algorithm based on pixel-value coding is proposed, which maps pixel values from [0, 255] to [-9, 9] and then replaces the high-frequency sub-band coefficients of the up-sampled image. During the embedding process, no auxiliary information is generated, which is a key aspect for user-friendliness. Extensive experimental results demonstrate that our method is capable of customizing satisfactory steganography strategies for various user requirements. Moreover, our method outperforms many state-of-the-art methods in terms of quality and security, i.e., lossless recovery of secret images, PSNR of 40 dB for stego-images, evasion from detection by six classic steganalysis tools.