A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking.

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations.

Lightweight 3-D Convolutional Occupancy Networks for Virtual Object Reconstruction.

The increasing demand for edge devices causes the necessity for recent technologies to be adaptable to nonspecialized hardware. In particular, in the context of augmented, virtual reality, and computer graphics, the 3-D object reconstruction task from a sparse point cloud is highly computationally demanding and for this reason, it is difficult to accomplish on embedded devices. In addition, the majority of earlier works have focused on mesh quality at the expense of speeding up the creation process.

Lightweight and Energy-Aware Monocular Depth Estimation Models for IoT Embedded Devices: Challenges and Performances in Terrestrial and Underwater Scenarios.

The knowledge of environmental depth is essential in multiple robotics and computer vision tasks for both terrestrial and underwater scenarios. Moreover, the hardware on which this technology runs, generally IoT and embedded devices, are limited in terms of power consumption, and therefore, models with a low-energy footprint are required to be designed. Recent works aim at enabling depth perception using single RGB images on deep architectures, such as convolutional neural networks and vision transformers, which are generally unsuitable for real-time inferences on low-power embedded hardware.

Image and Video Forensics.

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security more and more [...

Identification of Social-Media Platform of Videos through the Use of Shared Features.

Videos have become a powerful tool for spreading illegal content such as military propaganda, revenge porn, or bullying through social networks. To counter these illegal activities, it has become essential to try new methods to verify the origin of videos from these platforms. However, collecting datasets large enough to train neural networks for this task has become difficult because of the privacy regulations that have been enacted in recent years.

An Evaluation of Entropy Measures for Microphone Identification.

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori.

Detection of manipulations on printed images to address crime scene analysis: A case study.

Photographic documents both in digital and in printed format plays a fundamental role in crime scene analysis. Photos are crucial to reconstruct what happened and also to freeze the fact scenario with all the different present objects and evidences. Consequently, it is immediate to comprehend the paramount importance of the assessment of the authenticity of such images, to avoid that a possible malicious counterfeiting leads to a wrong evaluation of the circumstance.