In recent years, the growth of the Internet along with multimedia data delivery, have caused the misuse and even alteration of the original digital content. As a security mechanism, digital watermarking has been used to protect digital content, traditionally in copyright protection applications, and more recently digital watermarking has been used in content enhancement applications. Regardless of the application, digital watermarking schemes always cause some level of signal degradation. There are however, certain applications as in the military or medical fields, where loss of information cannot be tolerated during the reconstruction of host signals, therefore conventional watermarking schemes do not satisfy the quality requirements. In order to reconstruct signals without loss of data, reversible watermarking is employed. Recent research in reversible watermarking has focused on either, the improvement of embedding capacity or the reduction of perceptual impact of the schemes, but scarcely exploring the robustness of reversible schemes. In this survey, we provide a deep review of reversible watermarking schemes and propose a new classification outline based on the robustness point of view. Finally, some future directions for reversible watermarking research are presented and open problems in robust reversible watermarking are outlined.

Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named ‘Archives of Digital Morphology’ (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.

Vertebrate microfossils have broad applications in evolutionary biology and stratigraphy research areas such as the evolution of hard tissues and stratigraphic correlation. Classification is one of the basic tasks of vertebrate microfossil studies. With the development of techniques for virtual paleontology, vertebrate microfossils can be classified efficiently based on 3D volumes. The semantic segmentation of different fossils and their classes from CT data is a crucial step in the reconstruction of their 3D volumes. Traditional segmentation methods adopt thresholding combined with manual labeling, which is a time-consuming process. Our study proposes a deep-learning-based (DL-based) semantic segmentation method for vertebrate microfossils from CT data. To assess the performance of the method, we conducted extensive experiments on nearly 500 fish microfossils. The results show that the intersection over union (IoU) performance metric arrived at least 94.39 %, meeting the semantic segmentation requirements of paleontologists. We expect that the DL-based method could also be applied to other fossils from CT data with good performance.

We propose generalized 3D shape descriptor for efficient classification of 3D archaeological artifacts Our descriptor is based on a Multi-View approach of curvature features, consisting of the following steps: Pose normalization of 3D models, Local curvature descriptor calculation, Construction of 3D shape descriptor using the Multi-View approach and curvature maps, and Dimensionality reduction by Random Projections. We generate two descriptors from two different paradigms: a) Hand-Crafted, wherein the descriptor is manually designed for object feature extraction, and directly passed on to the classifier; and b) Machine Learnt, in which the descriptor is automatically learns the object features through a pretrained Deep Neural Network model (VGG-16) for Transfer Learning and passed on to the classifier. These descriptors are applied to two different archaeological datasets: (a) non-public Aztec dataset, represented by a collection of 963-3D archaeological objects from the Templo Mayor Museum in México City, that includes anthropomorphic sculptures, figurines, masks, ceramic vessels, and musical instruments; and (b) 3D Pottery Content Based Retrieval Benchmark dataset, consisting of 411 objects. Once the Multi- View descriptors are obtained, we evaluate their effectiveness by using the following object classification schemes: K-Nearest Neighbor, Support Vector Machine and Structured Support Vector Machine. Our object descriptors classification results are compared against 5 popular 3D descriptors in the literature, namely Rotation Invariant Spherical Harmonic, Histogram of Spherical Orientations, Signature of Histograms of OrienTations, Symmetry Descriptor, and Reflective Symmetry descriptor. Experimentally, we were able to verify that our machine learnt and hand-crafted descriptors offer the best classification accuracy (20% better on average than comparing descriptors), independently of the classification methods. Our proposed descriptors are able to capture sufficient information to discern among the different classes, concluding that adequately characterizes the datasets.

Smartphones penetration-rate continues expanding, from 44% in 2017 up to 59% expected increase in 2022 as reported by Strategy Analytics. At present, smartphones dominate the global mobile traffic, which in turn is dominated by video communications. For mobile systems with limited power capabilities, the processing of real-time multimedia information with affixed security represents a real challenge. In this work, we propose a high-performance encryption scheme capable of running on low-power smartphones without holding back video coding operation. We are aimed at destroying the meaning of the entropy coded bitstream by inserting random bit errors to induce error propagation and impede natural self-resynchronization process. The scheme consists of three main process: 1) A new integer chaos-based Coupled-Map Lattice -CML for creating secure pseudo-random trajectories; 2) Random bit flipping of the bitstream based on a Dynamic Reference Point (DRP) not exposed to attackers; and 3) Random selection of CML byte-trajectories for both DRP and bit flipping processes for increased security. Implementation of the scheme on smartphones with different CPU-power capabilities shows excellent performance to handle high-bandwidth real-time video smoothly (one-to-one and group calls). The scheme provides high scalable security with encrypted data volume fluctuating between 0.7%-3% of the total compressed data (video and still images).