3D steganalysis aims to identify stego models by detecting subtle geometric modifications. Traditional steganalysis algorithms are based on manually designed feature sets and machine learning but struggle to leverage classification results to optimize feature extraction. To address this, we propose a multi-resolution, end-to-end point cloud steganalysis algorithm. The model integrates a key point selection method based on Gaussian curvature to identify steganalysis-sensitive points. By selecting key points, this method standardizes irregular point cloud data, addressing non-uniform input points, and ensuring universality across datasets. The feature mining block, which comprises local starnet fusion and feature refining block, enhances the detection of subtle steganographic signals. Additionally, the multi-resolution network combines basic feature extraction with feature mining block, enhancing multi-level sampling capabilities and preventing the loss of weak signals during sampling. Experimental results demonstrate that the proposed algorithm effectively learns steganographic features, accurately detects hidden information in point clouds, and achieves high performance on benchmark datasets.

ABSTRACT In order to address the challenge of small target recognition in traffic scenes, we propose a model based on you only look once version 8X (Yolov8X) network model, which has been combined with receptive fields block (RFB) and multidimensional collaborative attention (MCA). First, the model employs the RFB to extract reliable and distinctive features, thereby enhancing the precision of small target identification. Furthermore, the MCA structure is introduced to simulate multidimensional attention through three parallel branches, thereby enhancing the feature expression ability of the model. This fragment describes a compression transformation and an excitation transformation that captures the differentiated feature representation of the command. These transformations facilitate the network's ability to locate and predict the location of small objects more accurately. Utilizing these transformations enhances the expressiveness and diversity of features, thereby improving the detection performance of small objects. Furthermore, data augmentation and hyperparameter optimization techniques are employed to enhance the model's generalisability. The validation results on the Argoverse 1.1 autonomous driving dataset demonstrate that the enhanced network model outperforms the prevailing detectors, achieving an F1 score of 78.6, an average precision of 55.1, and an average recall of 72.4. The algorithm's excellent performance for small target detection was demonstrated through visual analysis, proving its high application value and potential for promotion in fields such as autonomous driving.

Three-dimensional (3D) point cloud information hiding algorithms are mainly concentrated in the spatial domain. Existing spatial domain steganalysis algorithms are subject to more disturbing factors during the analysis and detection process, and can only be applied to 3D mesh objects, so there is a lack of steganalysis algorithms for 3D point cloud objects. To change the fact that steganalysis is limited to 3D mesh and eliminate the redundant features in the 3D mesh steganalysis feature set, we propose a 3D point cloud steganalysis algorithm based on composite operator feature enhancement. First, the 3D point cloud is normalized and smoothed. Second, the feature points that may contain secret information in 3D point clouds and their neighboring points are extracted as the feature enhancement region by the improved 3DHarris-ISS composite operator. Feature enhancement is performed in the feature enhancement region to form a feature-enhanced 3D point cloud, which highlights the feature points while suppressing the interference created by the rest of the vertices. Third, the existing 3D mesh feature set is screened to reduce the data redundancy of more relevant features, and the newly proposed local neighborhood feature set is added to the screened feature set to form the 3D point cloud steganography feature set POINT72. Finally, the steganographic features are extracted from the enhanced 3D point cloud using the POINT72 feature set, and steganalysis experiments are carried out. Experimental analysis shows that the algorithm can accurately analyze the 3D point cloud’s spatial steganography and determine whether the 3D point cloud contains hidden information, so the accuracy of 3D point cloud steganalysis, under the prerequisite of missing edge and face information, is close to that of the existing 3D mesh steganalysis algorithms.

Aiming at the problem that most of the existing embedding carriers of information hiding are single two-dimensional images with limited embedding information capacity, a multicarrier information hiding algorithm based on hierarchical compression of 3D point cloud model is proposed. First, the minimum bounding box of the model is generated and the model slices are layered. Secondly, the carrier set is classified. Finally, the secret information is hidden. Experimental results show that the robustness of the proposed algorithm is significantly improved compared with the comparison algorithm when facing a single attack.