Image steganalysis detects hidden information within images. However, existing methods are primarily designed for low-resolution images and they struggle to address the challenges posed by the widespread use of high-resolution images in real-world scenarios such as communications and social media. Under the secure embedding constrained by the square root law, the steganographic noise of high-resolution images is significantly diluted, thereby making “strong decision regions” critical to detection increasingly scarce, whereas the interference effect of “weak decision regions” is relatively prominent. Existing methods treat all areas equally, making it difficult to fully utilize strong decision regions and suppress the negative impact of weak decision regions. To address these issues, we propose the HRIS, a two-phase collaborative optimization framework for high-resolution steganalysis from “discovery” to “utilization”. In the “discovery” phase, we propose a dynamically contribution-guided decision region recognition mechanism. This mechanism employs a difference amplification module to amplify the steganographic noise differences between regions and then leverages a cooperative game-driven dynamic optimization strategy to compute each sub-region's contribution to the prediction. It accurately identifies and reinforces strong decision regions while suppressing interference from weak decision regions, resulting in significantly improved local detection accuracy. In the “utilization” phase, we propose a decision regions-global steganographic features bidirectional collaborative optimization framework that leverages the identified strong and weak decision regions to direct the extraction of global steganographic noise features. These global features are then fed back to refine the local feature representations, enabling collaborative enhancement between local and global analyses. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on high-resolution images.

In recent years, video steganography technique based on H.265/HEVC has received widespread attention. Typically, video steganography selects various syntax elements during the encoding process as carriers, and utilizing Prediction Unit (PU) as carrier is currently one of the most significant research directions. However, due to the limited number of PU types, such algorithms often suffer from insufficient capacity and visual quality. To alleviate the aforementioned issues, this paper proposes an H.265/HEVC video steganography algorithm that utilizes polygon encoding and Improved Deep Learnable Similarity Network Filter (IDLSNF). Firstly, we design a new polygon encoding rule, which maps different integers into several polygons. Secondly, we propose a novel steganography method based on polygon encoding and PU partition mode. This method selects the PUs of$8\times 8$and$16\times 16$coding unit in P-frames as carriers and hides the secret message by modifying the partition mode of two adjacent PUs. Due to the ability of polygon encoding to represent more information within a small range, it increases capacity with low steganographic distortion. Thirdly, we further propose a filter by improving DLSN, which enhances the visual quality of the entire stego video by processing I-frames. Extensive experimental results show that the video steganography algorithm proposed in this paper achieves higher capacity and superior visual quality compared to current State-of-the-Art methods. Meanwhile, our algorithm can also obtain good BRI and anti-steganalysis performance. This method has promising application prospects in the field of video covert communication.

Image steganalysis is a detection task to distinguish whether a secret message is embedded in a digital image. Due to the domain inconsistency caused by Cover Source Mismatch(CSM) and Steganographic Algorithm Mismatch (SAM), most of them suffer from significant performance degradation. Recent mismatched steganalysis focused on extracting domain invariant features by domain adversarial training or feature alignment. However these schemes are limited to unstable performance in diverse domain mismatch scenarios, and are even ineffective in some cases. In this paper, we propose a Universal Mismatched Steganalysis PCD-UMS via pair-wise confidence difference-based pseudo-label selection from the perspective of optimizing target training data. Specifically, we reveal a strong positive correlation commonality between pair-wise confidence difference and the detection performance of steganalysis among various mismatch scenarios. Based on this, a novel pseudo-label selection strategy consisting of maximum confidence difference first (MCDF) rule and pair-wise label differential storage (PLDS) rule is designed to select and filter the reliable target pseudo-labels. Furthermore, a multi-perspective pair-wise feature alignment loss is designed to initially transfer the classification ability of source steganalysis, thus solving the problem that source steganalysis fails completely under some domain mismatch scenarios. Comprehensive experiments show that our PCD-UMS outperforms the existing mismatched steganalysis by 12.07% and 3.40% in terms of detection performance under CSM and SAM scenarios.

Cost-based image steganography can significantly enhance its performance through a Reinforcement Learning (RL) framework. However, existing methods still face limitations in the generation of reward signals and the capture of image texture details. To address these challenges, this paper proposes a Dual-Branch Texture Enhancement Reinforcement Learning framework (DBT-RL) for symmetric embedding cost learning. This framework incorporates a Texture Information Enhancement Module (TIEM), enabling the policy network to more effectively focus on complex textured regions. Additionally, DBT-RL integrates multiple steganalysis to construct an ensemble environment network and introduces a novel adaptive update strategy. This strategy dynamically selects the best-performing steganalyzer to provide rewards to the policy network while self-updating weaker steganalyzers, ensuring that the policy network receives precise and dynamically balanced feedback. A large number of experimental results show that DBT-RL achieves high performance in the security of symmetric-cost embedding steganography.

Recent advances in steganography leverage generative adversarial networks (GANs) as a robust framework for securing covert communications through adversarial training between stego-generators and steganalytic discriminators. This paradigm facilitates the synthesis of secure steganographic images by harnessing the competition between network components. However, existing GAN-based approaches suffer from asymmetric capacity between generators and discriminators: suboptimally trained discriminators provide inadequate gradient guidance for generator optimization, causing premature convergence and security degradation. To overcome this critical limitation, we propose an enhanced multi-steganalyzer adversarial architecture incorporating maximum mean discrepancy (MMD) regularization. Our framework introduces two key innovations: 1) an MMD-based regularization mechanism mitigating distributional discrepancies among multiple steganalyzers through kernel embedding optimization, and 2) a reward function with fusing gradients derived from multiple steganalyzers to boost reinforcement learning-based adversarial training. This dual strategy enables the discriminator to learn generalized forensic features while maintaining equilibrium in adversarial training dynamics, ultimately allowing the generator to produce stego images resistant to multiple steganalyzers simultaneously. Comprehensive experiments validate our method’s superiority: When evaluated across five steganalysis networks, including YedNet, CovNet, LWENet, SRNet, and SwT-SN, at 0.1-0.4 bpp payloads, the proposed framework achieves improvements in average detection error rates over state-of-the-art techniques such as SPAR-RL and GMAN. Ablation studies further confirm that MMD regularization contributes significantly to security enhancement.

The purpose of deep image hiding is to embed the secret image imperceptibly in an equally sized cover image, and then recover the secret image almost perfectly at the receiver end. How to improve the quality of recovered secret images while ensuring the visual quality and security of stego images is an important challenge. In order to address this issue, a novel deep image hiding framework called EFCA-DIH (Edge Features and Coordinate Attention-based Invertible Network for Deep Image Hiding) is proposed. Firstly, an important feature extraction module is proposed to extract wavelet sub-band features coupled with edge features, thereby hiding the secret image better in the cover image. Secondly, a coordinate attention mechanism is introduced into the invertible hidden module to embed the secret information in the complex texture regions. Finally, an edge feature loss function is designed to constrain the edge differences between the stego image and the cover image, and between the secret image and the recovered secret image, thereby improving the quality of both the stego image and the recovered secret image. Experimental results have demonstrated that our EFCA-DIH significantly improves the quality of recovered secret images compared with other state-of-the-art methods, while maintaining the visual quality and security of stego images.

Non-convertible tokens (NFTs) have become a fundamental part of the metaverse ecosystem due to its uniqueness and immutability. However, existing copyright protection schemes of NFT image art relied on the NFTs itself minted by third-party platforms. A minted NFT image art only tracks and verifies the entire transaction process, but the legitimacy of the source and ownership of its mapped digital image art cannot be determined. The original author or authorized publisher lack an active defense mechanism to prove ownership of the digital image art mapped by the unauthorized NFT. Therefore, we propose a self-defense copyright protection scheme for NFT image art based on information embedding in this article, called SDCP-IE. The original author or authorized publisher can embed the copyright information into the published digital image art without damaging its visual effect in advance. Different from the existing information embedding works, the proposed SDCP-IE can generally enhance the invisibility of copyright information with different embedding capacity. Furthermore, considering the scenario of copyright information being discovered or even destroyed by unauthorized parties, the designed SDCP-IE can efficiently generate enhanced digital image art to improve the security performance of embedded image, thus resisting the detection of multiple known and unknown detection models simultaneously. The experimental results have also shown that the PSNR values of enhanced embedded image are all over 57db on three datasets BOSSBase, BOWS2, and ALASKA#2. Moreover, compared with existing information embedding works, the enhanced embedded images generated by SDCP-IE reaches the best transferability performance on the advanced CNN-based detection models. When the target detector is the pre-trained SRNet at 0.4 bpp, the test error rate of SDCP-IE at 0.4 bpp on the evaluated detection model YeNet reaches 53.38%, which is 4.92%, 28.62%, and 7.05% higher than that of the UTGAN, SPS-ENH, and Xie-Model, respectively.

Adversarial embedding for image steganography is a novel technology to effectively enhance the steganographic security of the traditional steganographic algorithms. However, the existing schemes still have room for further improvement in the design of optimization strategy and the steganographic post-processing of optimization failure. In this paper, we design the progressive probability optimizing strategy (PPO). It dynamically selects more efficient gradients to guide the optimization of the probability optimization in a progressive manner. Moreover, we propose a discarded stego recycling mechanism (DSR) to re-select the stego from the discarded stego set that have failed to deceive the target steganalyzer after the optimzation fails. In such way, the statistical distribution of the stego can still further approximate the cover, thus further improving the steganographic security on re-trained steganalyzers in adversary-aware scenario. Comprehensive experiments show that compared with the existing advanced schemes, the proposed method boosts the security improvement against both the re-trained hand-crafted feature-based and deep leanring-based steganalysis models.

Recently, the asymmetric cost-based steganographic method using generative adversarial networks has achieved significant success. This highlights the substantial potential of deep learning-based asymmetric cost generation methods over traditional methods reliant on cost enhancement. However, the current frameworks for asymmetric cost learning ignore the correlation between positive and negative embedding costs, resulting in an imbalance asymmetric embedding costs. This can cause scattered modified pixels or even anomalous modified pixels in the stego image, thereby reducing steganographic security. In this paper, we propose a novel asymmetric steganographic cost learning framework, termed Steganographic embedding Cost Generation and Modulation (SCGM), to ensure a balance between asymmetric embedding costs by maintaining the correlation and therefore improve steganographic security. In our framework, we initially train a policy network to produce symmetric costs and subsequently use an adaptive modulation module we designed to achieve asymmetry. The modulation module facilitates the adaptive transformation of learned symmetric costs into asymmetric costs by autonomously learning modulation proportions during adversarial training with steganalysis. Moreover, we develop distinct adversarial loss functions for both the symmetric cost generation and the asymmetric cost modulation phases to further enhance steganographic security. Extensive experimental results have demonstrated that SCGM attains state-of-the-art performance in steganographic security, with an average error rate across steganalyzers that exceeds the existing best asymmetric cost-based steganography method by 2.77%.

Deep image hiding is a challenging image processing task that aims to hide a secret image into a cover image of equal size perfectly. How to improve the imperceptibility of deep image hiding while ensuring high computational efficiency is a primary challenge. Where imperceptibility means not being visually perceived while not being perceived by the steganalysis model. In this paper, we propose a novel deep image hiding framework called DIH-OAIN (Deep Image Hiding based on One-way Adversarial Invertible Networks) to address it. Firstly, an image cascade framework is introduced to extract image semantics and details with dual-resolution branches, and reduces computation complexity by balancing image resolution and model complexity. Secondly, a hidden probability guided module is designed to constrain the secret image to be hidden in the texture region, utilizing the image texture complexity as prior knowledge. The above two points can effectively improve visual imperceptibility. Finally, a one-way adversarial training strategy is proposed to enhance the model imperceptibility. A series of experimental results show that the proposed method is significantly improved in imperceptibility comparing to state-of-the-art deep image hiding algorithms, while maintaining a low computation complexity.

Adversarial example-based steganographic methods that utilize the gradients of target steganalyzer to update symmetric costs are emerging. The existing adversarial adjustment strategies for costs still have limited improvements in steganographic security. The existing gradient selection scheme, which sets a fixed gradient selection ratio for all images, is not delicate enough. To address the above problems, this paper proposes an iterative two-stage probability adjustment strategy with a progressive incremental searching mechanism (ITPA-PIS) to further improve the security of updated asymmetric distortions. Unlike previous works that adopted the cost as the adjustment object, we explore a new adjustment object, i.e., probability, and then design an iterative two-stage probability adjustment strategy (ITPA) to obtain a more secure asymmetric distortion, thereby improving the anti-detection performance of the traditional symmetric distortion algorithms against deep learning-based steganalyzers. In addition, we specifically design a progressive incremental searching mechanism (PIS) to select partially efficient gradients to guide the probability adjustment. Unlike existing gradient selection schemes that manually set a fixed selection ratio, PIS adopts a progressive searching method to dynamically determine the gradient selection ratio suitable for each image, thereby enhancing the overall performance of the proposed ITPA again. The experimental results show that our proposed ITPA-PIS achieves outstanding security performance on the CNN-based steganalysis models XuNet, YedroujNet, SRNet, and EfficientNet and hand-crafted feature-based steganalysis models SRM and MaxSRMd2 under the adversary unawareness and adversary awareness scenarios.

To achieve high tampering localization precision and low distortion, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is proposed based on an improved region nesting and a novel watermark generation. First, an intensive investigation is performed to the recently proposed region nesting (RN) partition. It is found that the original vertex and its mapped one cannot be guaranteed to be on the same line, which means that it still has room for improvement in term of distortion. Based on this, an improved region nesting partition (IRN) is proposed. Secondly, inspiring by the idea of soldiers parading, a novel watermark generation based on the short hash of the adjacent geometric features is developed. Then, a new coordinate system is constructed to achieve invariability of translation, scaling, rotation and entity re-arrangement. Based on the above techniques, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is put forward. Experimental results and analysis show that IRN can obtain at least 15% distortion reduction compared with RN, the precision of tampering localization can be improved to a single vertex, and a certain semi-fragility in translation, scaling, rotation, and entity re-arrangement can be achieved. Furthermore, it has no file size expansion, and can obtain a good balance among imperceptibility, semi-fragility and tampering localization precision.

Designing selective encryption (SE) schemes for H.265/HEVC has been attracted much attention with the advent of H.265/HEVC codec in the past two decades. However, the most SE algorithms for H.265/HEVC encrypt the syntax elements in the bypass mode to keep the bit rate. Moreover, the edge region of the video data is not sufficiently protected. To produce large visual distortion and edge loss, a tunable SE scheme for H.265/HEVC based on the chroma intra prediction mode (IPM) and coefficient scrambling is proposed. First, a pseudo-random number sequence is generated by AES-CTR. Then, the prediction, residual, and reconstruction information in the H.265/HEVC encoding process is encrypted by a pseudo-random sequence. It encrypts the syntax elements of context-based adaptive binary arithmetic coding (CABAC) in the bypass mode. Some syntax elements, including chroma IPM in the regular mode, are encrypted as well. To further protect the edge information, a coefficient scrambling is adopted. The edge information of each frame is extracted and the transform units (TUs) are classified according to it. Then, the coefficients of the TUs containing edge are scrambled. Finally, a sign used for marking the type of each TU is embedded into a coefficient. The experimental results and analysis show that the proposed scheme has better visual distortion and subjective evaluation results compared with some existing H.265/HEVC SE algorithms. Meanwhile, users can flexibly use the proposed SE scheme according to encryption performance and bit rate requirements, which is attractive in the scenario of protecting video in cloud servers.

Currently, much attention has been paid to reversible data hiding (RDH) in an encrypted domain due to the popular deployment of cloud storage. However, nearly all existing RDH schemes in the encrypted domain are proposed for raster images, and very little work has been done to 2D vector graphics, which are represented in real numbers. In this paper, a reversible mapping model for real numbers is first built. It maps the points in Rnto 2snon-intersecting subsets in Rn, which guarantees that s bits can be embedded into each real number. Based on the model, an RDH scheme in encrypted 2D vector graphics is put forward. In the scheme, a user encrypts 2D engineering graphics and stores them in the cloud, and then the cloud service provider can perform information hiding, extraction, and even recover the encrypted 2D vector graphics. For the authorized user, it can acquire the recovered 2D vector graphics from the cloud and obtain their original versions after decryption. For an unauthorized user, he can only acquire the encrypted 2D vector graphics with a hidden message, and only approximate 2D vector graphics can be obtained even if he knows the decryption key but does not know the hiding key. The experimental results and analysis show that it can strike a good balance between security, distortion, and capacity. It provides a new paradigm for RDH in the encrypted domain for the data represented in real numbers.

In order to improve the robustness and capability of resisting image steganalysis, a novel coverless image steganography algorithm based on discrete cosine transform and latent dirichlet allocation (LDA) topic classification is proposed. First, latent dirichlet allocation topic model is utilized for classifying the image database. Second, the images belonging to one topic are selected, and 8 × 8 block discrete cosine transform is performed to these images. Then robust feature sequence is generated through the relation between direct current coefficients in the adjacent blocks. Finally, an inverted index which contains the feature sequence, dc, location coordinates, and image path is created. For the purpose of achieving image steganography, the secret information is converted into a binary sequence and partitioned into segments, and the image whose feature sequence equals to the secret information segments is chosen as the cover image according to the index. After that, all cover images are sent to the receiver. In the whole process, no modification is done to the original images. Experimental results and analysis show that the proposed algorithm can resist the detection of existing steganalysis algorithms, and has better robustness against common image processing and better ability to resist steganalysis compared with the existing coverless image steganography algorithms. Meanwhile, it is resistant to geometric attacks to some extent. It has great potential application in secure communication of big data environment.