Image steganalysis methods aim at detecting whether there exist hidden messages in images. Deep learning (DL) models have been proposed to enhance steganography detection. These models occupy a large amount of memory and, for this reason, should be optimized when the scenario involves resource-limited devices and systems. This work addresses different deep learning model optimization strategies, namely model pruning, quantization and weight clustering, applied to a deep learning model that presents competitive accuracy results in image steganalysis and belongs to the family of DL models with smaller memory requirements. The results show that the use of optimization schemes can lead to similar or even better accuracy compared to the original model (without the use of optimization schemes), while requiring less memory to store the model. Different scenarios are simulated for each optimization technique, and, finally, quantization is combined with pruning. For dynamic range quantization (DRQ), we achieve models that can save approximately 72% of storage. For FP16 quantization, we obtain better accuracy results and a model with approximately 50% less memory consumption. By applying weight clustering, we also achieve compressed models that can save more than 72% of storage space and lead to better accuracy for some scenarios. Using the combination of pruning and quantization, smaller models in terms of memory requirements are obtained.

This article aims to review encryption techniques for 3D or higher-dimensional images. Precisely, the following classes of images are considered: light field images, point clouds, and 3D medical images. The security and performance aspects of the encryption schemes for images are analyzed. It is observed that the number of papers with encryption in the transform domain exceeds the number of papers with encryption in the spatial domain. Additionally, it is observed that the application of encryption does not predominate in all the dimensions of the images, that is, the encryption does not occur completely in the image but rather through slices or sections. This review article summarizes several issues related to encryption techniques, technical perspectives for the future, and gaps in the literature.

In the realm of product design and development, efficient retrieval and reuse of 3D CAD models are vital for optimizing workflows and minimizing redundant efforts. Manual labeling of CAD models, while traditional, is labor-intensive and prone to inconsistency, highlighting the need for automated classification systems. Multi-view convolutional neural networks (MVCNNs) offer an automated solution by leveraging 2D projections to represent 3D objects, balancing high classification accuracy with computational efficiency. Despite their effectiveness, the computational demands of MVCNNs pose challenges in large-scale CAD applications. This study investigates the use of optimization strategies, precisely pruning and quantization, in the scenario of MVCNN applied to the classification of 3D CAD mechanical models. By using different pruning and quantization strategies, we evaluate trade-offs between classification accuracy, execution time, and memory usage. In our evaluation of pruning and quantization techniques, 8-bit quantization reduced the memory used by the model from 83.78 MB to 21.01 MB, with accuracy only slightly decreasing from 93.83% to 93.59%. When applying 25% structured pruning, the model’s memory usage was reduced to 47.16 MB, execution time decreased from 133 to 97 s, and accuracy decreased to 92.14%. A combined approach of 25% pruning and 8-bit quantization achieved even better resource efficiency, with memory usage at 11.86 MB, execution time at 99 s, and accuracy at 92.06%. This combination of pruning and quantization leads to efficient MVCNN model optimization, balancing resource usage and classification performance, which is especially relevant in large-scale applications.

Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm intelligence. Initialization of the Linde–Buzo–Gray (LBG) algorithm, which is the most popular VQ codebook design algorithm, is a step that directly influences VQ performance, as the convergence speed and codebook quality depend on the initial codebook. A widely used initialization alternative is random initialization, in which the initial set of codevectors is drawn randomly from the training set. Other initialization methods can lead to a better quality of the designed codebooks. The present work evaluates the impacts of initialization strategies on swarm intelligence algorithms for codebook design in terms of the quality of the designed codebooks, assessed by the quality of the reconstructed images, and in terms of the convergence speed, evaluated by the number of iterations. Initialization strategies consist of a combination of codebooks obtained by initialization algorithms from the literature with codebooks composed of vectors randomly selected from the training set. The possibility of combining different initialization techniques provides new perspectives in the search for the quality of the VQ codebooks. Nine initialization strategies are presented, which are compared with random initialization. Initialization strategies are evaluated on the following algorithms for codebook design based on swarm clustering: modified firefly algorithm—Linde–Buzo–Gray (M-FA-LBG), modified particle swarm optimization—Linde–Buzo–Gray (M-PSO-LBG), modified fish school search—Linde–Buzo–Gray (M-FSS-LBG) and their accelerated versions (M-FA-LBGa, M-PSO-LBGa and M-FSS-LBGa) which are obtained by replacing the LBG with the accelerated LBG algorithm. The simulation results point out to the benefits of the proposed initialization strategies. The results show gains up to 4.43 dB in terms of PSNR for image Clock with M-PSO-LBG codebooks of size 512 and codebook design time savings up to 67.05% for image Clock, with M-FF-LBGa codebooks with size $N=512$, by using initialization strategies in substitution to Random initialization.

Ocultação de informação é um tema que tem despertado o interesse de muitos pesquisadores, tendo sido abordado em dissertações de mestrado, teses de doutorado e em disciplinas de alguns cursos de gradução e de pós-graduação das áreas de Engenharia Elétrica e de Ciência da Computação. A ocultação de informação abrange áreas como esteganografia e marca d’água. Há um amplo espectro de aplicações associadas a essas técnicas, que vão desde comunicações secretas até direitos autorais (copyright) de objetos digitais. Este artigo apresenta fundamentos básicos de esteganografia e marca d’água em imagens digitais. São abordadas técnicas de esteganálise, bem como aplicações e aspectos de marcas d’água frágeis e robustas. É apresentado um breve levantamento de ferramentas que podem ser usadas como apoio ao ensino dessas técnicas. O artigo apresenta, particularmente, cenários em que ficam claros os benefícios das ferramentas para os estudantes.