Head avatar generation is facilitated to construct high-fidelity 3D virtual personas from a single portrait, but it also raises the risk of unauthorized personal avatars generation. Recent 2D portrait protection methods actively prevent malicious image generation by perturbing the identity features. However, there are two key limitations when directly applied to prevent 3D head avatar generation: 1) These methods neglect the inherent 3D geometric structure of portrait, thus failing to disrupt the modeling of 3D shapes or poses. 2) They focus only on identity offset and are unable to interfere with the overall appearance, resulting in excessive preservation of facial characteristics. To overcomes these limitations, we propose a 3D defense framework termed Anti-Avatar, tailored to protect against unauthorized 3D head avatar generation from a single portrait. Specifically, Anti-Avatar consists of two key designs: Geometric Disruption and Perceptual Confusion. The former disrupts the precise reconstruction of 3D structure by interfering with the estimation of geometric parameters, thus affecting the structural accuracy of the 3D avatar. Collaboratively, the latter confuses image features by dispersing attention distribution, thereby hindering the effective perception of portrait appearance. Benefiting from the above dual-space divergence in geometry and perception, the avatars generated by our protected portraits exhibit substantial discrepancies from the originals. Extensive experiments show that our Anti-Avatar outperforms 2D methods in protection performance and effectively resists reconstruction and manipulation by state-of-the-art 3D head avatar generation methods.

3D Gaussian Splatting (3DGS) has recently achieved remarkable progress in novel view synthesis. However, existing methods rely heavily on high-quality data for rendering and struggle to handle degraded scenes with multi-view inconsistency, leading to inferior rendering quality. To address this challenge, we propose a novel Depth-aware Gaussian Splatting for efficient 3D scene Restoration, called RestorGS, which flexibly restores multiple degraded scenes using a unified framework. Specifically, RestorGS consists of two core designs: Appearance Decoupling and Depth-Guided Modeling. The former exploits appearance learning over spherical harmonics to decouple clear and degraded Gaussian, thus separating the clear views from the degraded ones. Collaboratively, the latter leverages the depth information to guide the degradation modeling, thereby facilitating the decoupling process. Benefiting from the above optimization strategy, our method achieves high-quality restoration while enabling real-time rendering speed. Extensive experiments show that our RestorGS outperforms existing methods significantly in underwater, nighttime, and hazy scenes.

3D editing with Gaussian splatting is exciting in creating realistic content, but it also poses abuse risks for generating malicious 3D content. Existing 2D defense approaches mainly focus on adding perturbations to single image to resist malicious image editing. However, there remain two limitations when applied directly to 3D scenes: (1) These methods fail to reflect 3D spatial correlations, thus protecting ineffectively under multiple viewpoints. (2) Such pixel-level perturbation is easily eliminated during the iterations of 3D editing, leading to failure of protection. To address the above issues, we propose a novel Defense framework against malicious 3D Editing for Gaussian splatting (DEGauss) for robustly disrupting the trajectory of 3D editing in multi-views. Specifically, to enable the effectiveness of perturbation across various views, we devise a view-focal gradient fusion mechanism that dynamically emphasizes the contributions of the most challenging views to adaptively optimize 3D perturbations. Furthermore, we design a dual discrepancy optimization strategy that both maximize the semantic deviation and the edit direction deviation of the guidance conditions to stably disrupt the editing trajectory. Benefiting from the collaborative designs, our method achieves effective resistance to 3D editing from various views while preserving photorealistic rendering quality. Extensive experiments demonstrate that our DEGauss not only performs excellent defense in different scenes, but also exhibits strong generalization across various state-of-the-art 3D editing pipelines.

Since the training data of the target model is not available in the black-box substitute attack, most recent schemes utilize generative adversarial networks (GANs) to generate data for training the substitute model. However, these GANs-based schemes suffer from low training efficiency as the generator needs to be retrained for each target model during the substitute training process, as well as low generation quality. To overcome these limitations, we consider utilizing the diffusion model (DM) to generate data and propose a novel data-free substitute attack scheme based on stable diffusion (SD) to improve the efficiency and accuracy of substitute training. Despite the data generated by the SD exhibited high quality, it presented a different distribution of domains and a large variation of positive and negative samples for the target model. For this problem, we propose latent code augmentation (LCA) to facilitate SD in generating data that aligns with the data distribution of the target model. Specifically, we augment the latent codes of the inferred member data with LCA and use them as guidance for SD. With the guidance of LCA, the data generated by the SD not only meets the discriminative criteria of the target model but also exhibits high diversity. By utilizing this data, it is possible to train the substitute model that closely resembles the target model more efficiently. Extensive experiments demonstrate that our LCA achieves higher attack success rates (ASRs) and requires fewer query budgets compared to GANs-based schemes for different target models. Our codes are available at https://github.com/LzhMeng/LCA.

3D Gaussian splatting has recently achieved remarkable progress in dynamic scene reconstruction. However, there remain two practical challenges: (1) Existing methods typically employ a strict point-wise deformation structure to model dynamic attributes, while neglecting the uncertain motion correlation in local space, leading to inferior adaptability to complex scenes. (2) The inherent low-frequency bias properties of Gaussians often lead to blurring artifacts due to the insufficient high-frequency learning of variable motions. To address these challenges, we propose a novel Frequency-aware Uncertainty Gaussian Splatting, termed FUGS, for adaptively reconstructing dynamic scenes in the Fourier space. Specifically, we design an Uncertainty-aware Deformation Model (UDM) that explicitly models motion attributes using learnable uncertainty relations with neighboring Gaussian points. Such a paradigm is capable of facilitating temporal and spatial motion correlation learning, thereby enabling flexible Gaussian deformations. Subsequently, a Dynamic Spectrum Regularization (DSR) is developed to perform coarse-to-fine Gaussian densification through low-to-high frequency filtering. By weighting the gradient with frequency distance, the Gaussian attribute is adaptively adjusted according to the scene complexity. Benefiting from the flexible optimization, our method achieves high-fidelity reconstruction of complex scenes while enjoying real-time rendering. Extensive experiments on synthetic and real-world datasets show that our FUGS exhibits significant superiority over state-of-the-art methods. The code will be available at https://github.com/KevinJoee/GS.

Semantic segmentation of remote sensing (RS) images refers to labeling each pixel with a class to identify objects or land cover types. Existing mainstream spatial-domain semantic segmentation methods are mainly categorized into convolutional neural network (CNN)-based and vision transformer (ViT)-based approaches. The former excels at capturing local features, while the latter is adept at extracting global features. Several recent approaches consider combining CNN and ViT to efficiently capture local and global features. However, these approaches still struggle to capture complete features of the RS images, resulting in inaccurate segmentation. To address this issue, we introduce the fast Fourier transform (FFT), which transforms images into the frequency domain for feature extraction, acquiring the image-size receptive field that can complement spatial-domain methods. Based on this, we propose a boundary-aware spatial and frequency dual-domain transformer, termed dual-domain transformer. Specifically, our dual-domain transformer incorporates a dual-domain mixer (DualM), where the spatial-domain branch combines depthwise convolution and the attention mechanism to extract local and global features effectively, while the frequency-domain branch uses FFT to extract image-size features. The two branches complement each other, enabling a more comprehensive feature extraction of RS images. Meanwhile, a boundary-guided training strategy utilizing a boundary-aware module (BAM) is devised to constrain the model extract and predict boundary detail texture, which is an auxiliary task. In addition, the decoder incorporates a scale-feature fusion module (SFM) for adaptive information fusion between the encoder and decoder. Comprehensive experiments on the Zeebrugge and ISPRS datasets, including Vaihingen and Potsdam, showcase that the dual-domain transformer significantly outperforms state-of-the-art (SOTA) methods.

Convolutional neural networks (CNNs) are known to be vulnerable to adversarial examples even in black-box scenarios, posing a significant threat to their reliability and security. Most existing black-box attack methods primarily focus on data-free scenarios, which often require a large number of queries and yield low attack success rates. But in practical applications, it is feasible to collect a small amount of data associated with the target network. In light of this, in this article, we propose an advancing few-shot black-box attack with alternating training scheme using few data and alternating training to improve the efficiency and attack success rate. Specifically, we propose an alternating training approach consisting of two parts, both aimed at optimizing the substitute network, which alternate and reinforce each other, leading to a significant reduction in the query budget required for a successful attack. In addition, we propose an image degradation (ID) module that expands the data volume diversity through ID techniques to mitigate the problem of generator overfitting. Furthermore, we design a model specific adapter to enable the substitute networks to dynamically adjust the parameters for different target networks. Extensive experiments demonstrate the efficacy of our approach in significantly reducing the query budget while achieving higher attack success rates compared to state-of-the-art competitors.

With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.