The essence of dynamic point cloud compression lies in the effective modeling of temporal context information, which poses significant challenges owing to the unstructured and sparse characteristics of point clouds. Existing dynamic compression methods exhibit a limited capacity to capture and leverage inter-frame information. Consequently, in this paper, we propose a Dynamic Point Cloud Compression framework with Spatio-Temporal Transformer-style Modeling (DPCC-STTM) to compress point cloud sequences within a latent space. To effectively extract and fully utilize temporal context, we introduce a spatio-temporal transformer-style modeling module, which performs effective modeling of the rich temporal information based on the correlation of temporal content. Furthermore, we introduce a multi-scale temporal processing module that captures temporal correlations across short and long ranges of multi-frame point clouds. This module also fuses modeled temporal information to enhance the prediction accuracy of potential features for the current frame. Extensive experiments demonstrate the superiority of our proposed framework, validated through both objective evaluation and subjective perception.

Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal prediction theory to enhance compression performance by designing high efficient intra and inter prediction strategies and compressing video frames one by one. In this paper, we propose a novel model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models the intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy through spatio-temporal predictions. To achieve this, we employ implicit neural representation as our basic modeling architecture. To improve the efficiency of video modeling, we first propose context-related spatial positional embedding and frequency domain supervision in spatial context enhancement. For temporal correlation capturing, we design the scene flow constrain mechanism and temporal contrastive loss. Extensive experimental results demonstrate that our method achieves up to a 20% bitrate reduction compared to the latest video coding standard H.266 and is more efficient in decoding than existing video coding strategies.

Point cloud compression has various purposes in different application scenarios, such as requiring visual fidelity in human vision tasks and pursuing semantic fidelity in machine vision tasks. To accommodate these diverse requirements, we propose a Human-Machine balanced point cloud geometry compression scheme (HM-PCGC) which considers the characteristics of various tasks. Our proposed scheme starts from a pre-trained, lightweight point cloud compression backbone and employs a Learned Semantic Mining module to aggregate multi-tasks features. By leveraging the aggregated features, HM-PCGC is able to retain the geometry and semantic properties of the point clouds during compression. To better balance between the signal distortion and semantic distortion, we integrated a multi-task learning mechanism during the training phase. Our approach is extensively evaluated and analyzed, and the results demonstrate its superiority over state-of-the-art traditional and deep learning based point cloud codecs for both signal reconstruction and machine vision tasks.

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size.Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying error. We propose an ensemble method with two decoupled attention paths, over the pixel domain and over that of our different manipulations, to learn the final fusion. Our results significantly improve over the state-of-the-art baselines and across varying noise levels.

Leakage models, regarded as a bridge between the physical signal and the sensitive operation, have a great influence on the effectiveness of the side channel analysis. The existing leakage models are usually divided into two categories, the non-profiled leakage models which have been chosen before sampling and analyzing, such as Hamming weight and Hamming distance, while the profiled leakage models, whose parameters have to be trained in the profiling phase, such as the Stochastic model of which both coefficient vector and pooled covariance matrix are required to be estimated based on the acquired samples. In general, a profiled leakage model is more accurate than a non-profiled one. However, it may lead to an inefficient attack if the leakage function is inaccurate, e.g., the over-fitting and under-fitting in the profiling phase. In this paper, we mathematically prove the relationship among different stochastic models, and propose a new method named ECM to solve the problem that much time is required to solve matrix in the profiling phase. Replacing the observations in the matrix solution with the average signals, the new method accelerates the construction of any stochastic model significantly, as long as the data-dependent signal has the property equal images under different subkeys. On the basis of theoretical results, we analyze the reasons why over-fitting and under-fitting happen, and quantify the condition when some of them occur. Finally, comparing with the existing construction method (HSS2012), we verify the effectiveness and efficiency of ECM with different metrics. Under the same accuracy, the ECM obviously has lower time complexity than HSS2012.

Password Authentication Protocol (PAP) is widely used in the Wireless Fidelity Point-to-Point Protocol to authenticate an identity and password for a peer. This paper uses a new knowledge-based framework to verify the PAP protocol and a fixed version. Flaws are found in both the original and the fixed versions. A new enhanced protocol is provided and the security of it is proved. The whole process is implemented in a mechanical reasoning platform, Isabelle. It only takes a few seconds to find flaws in the original and the fixed protocol and to verify that the enhanced version of the PAP protocol is secure.

Cryptographic protocols have been widely used to protect communications over insecure network environments. Existing cryptographic protocols usually contain flaws. To analyze these protocols and find potential flaws in them, the secure properties of them need be studied in depth. This paper attempts to provide a new framework to analyze and prove the secure properties in these protocols. A number of predicates and action functions are used to model the network communication environment. Domain rules are given to describe the transitions of principals' knowledge and belief states. An example of public key authentication protocols has been studied and analysed.