The existing deep-learning based robust watermarking model generally applies a discriminator to form generative adversarial network (GAN) for increasing the quality of encoded images, and adopts a single encoder to embed watermark. However, GAN training is unstable, and the single encoder cannot fully adjust the watermarking distribution, thus affecting the watermarking performance. To address those limitations, this paper presents the multi-encoder based on conditional diffusion model (CDM) for robust image watermarking, namely, DiffW. To enhance the stability, the multi-encoder structure based on CDM replaces GAN for optimizing the watermarking distribution iteratively. Specifically, the operation of each timestep in the forward and reverse diffusion processes of the CDM is regarded as an encoder to overcome the shortcomings of the single encoder structure. At the training stage, under the guidance of the conditional noisy image, the forward process trains each encoder to fuse the image and watermark to generate high-quality encoded images. During the testing stage, only a small number of trained encoders of the forward process are used, so as to reduce the time complexity. Furthermore, to improve watermarking robustness, the channel attention module (CAM) is designed to extract main watermark features by mining channel correlations for multi-layer fusion, so that watermark can be embedded into imperceptible and texture areas. The experimental results reveal that compared with the existing watermarking model, the proposed DiffW can achieve better results in terms of watermarking invisibility and robustness.

Multi-image steganography ensures privacy protection while avoiding suspicion from third parties by embedding multiple secret images within a cover image. However, existing multi-image steganographic methods fail to model global spatial correlations to reduce image damage at the low computation cost. Moreover, they do not account for the anti-distortion capability of the cover image, which is crucial for achieving imperceptible and ensuring security. To overcome these limitations, we propose StegMamba, a distortion-free immune-cover for multi-image steganography architecture with a state space model. Specifically, we first explore the potential of the linear computational cost model Mamba for data hiding tasks through a steganography Mamba block (SMB), whose efficiency makes it suitable for real-time applications. Subsequently, considering that images with distortion resistance reduce embedding damage, the original cover image is reconstructed through immune-cover construction module (ICCM) and associated with the steganography task. Moreover, well-coupled features facilitate fusion, and thus a wavelet-based interaction module (WIM) is designed for effective communication between the immune-cover and the secret images. Compared with the state-of-the-art global attention-based methods, the proposed StegMamba obtains PSNR gains of 3.30 dB, 1.37 dB, and 1.92 dB for the stego image, and two secret recovery images, respectively, and the reduction of 2.87% in detection accuracy for anti-steganalysis. This code is available athttps://github.com/YuhangZhouCJY/StegMamba.

Existing multimodal point cloud quality assessment (PCQA) methods usually integrate 3D and 2D information to simulate human visual perception of distortions. However, due to the lack of consideration of spatial correspondence, they have difficulty to learn consistent distortion representations from different modalities in the same region of the PC. In addition, they also ignore the heterogeneity of modalities and rely on complex fusion mechanisms (e.g., attention) to integrate multimodal features. Both lead to limited performance and increased computational complexity. To address these limitations, we propose a novel double alignment multimodal learning network (DA-Net), which introduces two key alignment strategies. Specifically, the first is spatial pre-alignment strategy, which generates informative 2D patch for each 3D patch via an adaptive patch projection module (APPM), ensuring accurate spatial correspondence of different modalities prior to feature extraction. The second is a uniform feature alignment strategy, which includes feature disentanglement module (FDM) and feature mapping module (FMM) to relieve heterogeneity of modalities and guide the optimization of 2D and 3D encoder. Finally, multimodal features are simply integrated and regressed to obtain the quality score. Experimental results demonstrate that the DA-Net exhibits outstanding performance and generalization ability. It also achieves lower computational complexity compared with other multimodal PCQA methods. The source codes of DA-Net will be available at https://github.com/Rphone/DA-Net.

As a crucial representation of 3D data, a point cloud (PC) can accurately capture the geometry, structure, and color information of objects. However, various quality problems arise owing to device noise, data acquisition errors, and compression algorithms, limiting the application of PCs. Therefore, assessing PC quality to determine its suitability for applications is a challenging task. In this work, a local and global structure-guided feature extraction and attention network (LGS-Net) is introduced for no-reference PC quality assessment (PCQA). This approach incorporates cluster construction (CC), local structure-guided cluster feature extraction (LSFE), and global structure-guided attention (GSA) modules. First, owing to the heightened sensitivity of the human visual system (HVS) to structural information, a graph filter is employed to identify high-frequency clusters. Within the LSFE module, a multiscale strategy is employed to ensure that structural information effectively influences both the geometry and color information. Simultaneously, the multiscale features within the cluster are dynamically fine-tuned using feature channel weight reassignment. To account for the impact of interclusters on overall quality, a GSA module is introduced to establish global dependencies between local clusters. This approach enables the extraction of final geometry, color, and structure information, which are ultimately used for accurate quality assessment. Extensive experimental results show that the proposed method outperforms the existing state-of-the-art PCQA methods using two publicly available subjective datasets.

Current Transformer-based image steganography cannot embed data properly without considering the correlation of the cover image and the secret image . In addition, to save computational complexity, spatial-wise Transformer is often used to apply in small spatial windows, which limits the extraction of the global feature. To solve those limitations, we present a channel-wise attention Transformer model for image steganography (CAISFormer), which aims to construct long-range dependencies for identifying inconspicuous positions to embed data. A channel self-attention module (CSAM) is deployed to focus the feature channels suitable for data hiding by establishing channel relationships. Meanwhile, a non-linear enhancement (NLE) layer is employed to enhance the beneficial features while weaken the irrelevant ones. For building feature coupling between the cover image and the secret image, a channel-wise cross attention module (CCAM) is designed to fine-tune cover image features by capturing their cross-dependencies. In addition, for concealing data properly, a global–local aggregation module (GLAM) is deployed to adjust fused features by combining global and local attention, which can focus on inconspicuous and texture regions, respectively. The experimental results demonstrate that CAISFormer obtains PSNR gains of more than 0.36 dB and 0.90 dB for the cover/stego image pair and the secret/recovery image pair, respectively, and the detection ratio is decreased by 3.43%, in single image hiding compared to the state-of-the-art. Moreover, the generalization ability is also proved across a variety of datasets. The code will be made publicly available at https://github.com/YuhangZhouCJY/CAISFormer .

Abstract Coloured point cloud (CPC) is one of the important representations of three‐dimensional objects, which has been used in many fields. CPC may encounter geometric and colour distortion during its compression, simplification or other processing. Thus, the objective visual quality assessment of CPC is one of the urgent issues to be resolved in the CPC's applications. Aiming at this problem, this paper proposes a new full‐reference visual quality assessment metric for CPC based on geometric segmentation and colour transformation (CPC‐GSCT), which analyzes geometric distortion and colour distortion of CPC. First, considering the visual masking effect of CPC's geometric information, CPC is segmented into different regions and distributed with different weights to describe the influence of visual masking effect in CPC quality assessment. At the same time, a geometric combination feature vector is defined and extracted for measuring the CPC's geometric distortion. Then, considering the colour perception of human eyes, a colour combination feature vector is extracted to measure the CPC's colour distortion in HSV colour space. Finally, all the extracted geometric and colour features are constituted as a feature vector to predict the quality of CPC. Experimental results on three databases (IRPC, SJTU‐PCQA and CPCD2.0) show that the proposed CPC‐GSCT metric can achieve better performance in predicting the visual quality of CPC than relevant existing methods.

Colored point cloud (PC) provides convenience for 3D digitization in the real world, but its huge amount of data needs to be compressed effectively. However, lossy compression will bring visual quality problems, so it is necessary to design reliable quality assessment methods. Considering the visual connection between 3D space and projection plane, we propose a new PC quality assessment (PCQA) method combining colored texture and curvature projection in this paper. Specifically, the colored texture information and curvature of colored PC are projected onto 2D planes to extract texture and geometric statistical features, respectively, so as to characterize the texture and geometric distortion. Experimental results on two colored PC databases (CPCD2.0 and IRPC) show that the proposed method has a good correlation with subjective quality scores and is superior to the state-of-the-art PCQA methods.