Improving Image Restoration with Soft-Rounding

Several important classes of images such as text, barcode and pattern images have the property that pixels can only take a distinct subset of values. This knowledge can benefit the restoration of such images, but it has not been widely considered in current restoration methods. In this work, we describe an effective and efficient approach to incorporate the knowledge of distinct pixel values of the pristine images into the general regularized least squares restoration framework. We introduce a new regularizer that attains zero at the designated pixel values and becomes a quadratic penalty function in the intervals between them. When incorporated into the regularized least squares restoration framework, this regularizer leads to a simple and efficient step that resembles and extends the rounding operation, which we term as soft-rounding. We apply the soft-rounding enhanced solution to the restoration of binary text/barcode images and pattern images with multiple distinct pixel values. Experimental results show that soft-rounding enhanced restoration methods achieve significant improvement in both visual quality and quantitative measures (PSNR and SSIM). Furthermore, we show that this regularizer can also benefit the restoration of general natural images.Comment: 9 pages, 6 figure

Reconstructive Neuron Pruning for Backdoor Defense

Deep neural networks (DNNs) have been found to be vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While existing defense methods have demonstrated promising results, it is still not clear how to effectively remove backdoor-associated neurons in backdoored DNNs. In this paper, we propose a novel defense called \emph{Reconstructive Neuron Pruning} (RNP) to expose and prune backdoor neurons via an unlearning and then recovering process. Specifically, RNP first unlearns the neurons by maximizing the model's error on a small subset of clean samples and then recovers the neurons by minimizing the model's error on the same data. In RNP, unlearning is operated at the neuron level while recovering is operated at the filter level, forming an asymmetric reconstructive learning procedure. We show that such an asymmetric process on only a few clean samples can effectively expose and prune the backdoor neurons implanted by a wide range of attacks, achieving a new state-of-the-art defense performance. Moreover, the unlearned model at the intermediate step of our RNP can be directly used to improve other backdoor defense tasks including backdoor removal, trigger recovery, backdoor label detection, and backdoor sample detection. Code is available at \url{https://github.com/bboylyg/RNP}.Comment: Accepted by ICML2

Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting

Federated learning has exhibited vulnerabilities to Byzantine attacks, where the Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and performance of the global model. A wealth of robust AGgregation Rules (AGRs) have been proposed to defend against Byzantine attacks. However, Byzantine clients can still circumvent robust AGRs when data is non-Identically and Independently Distributed (non-IID). In this paper, we first reveal the root causes of performance degradation of current robust AGRs in non-IID settings: the curse of dimensionality and gradient heterogeneity. In order to address this issue, we propose GAS, a \shorten approach that can successfully adapt existing robust AGRs to non-IID settings. We also provide a detailed convergence analysis when the existing robust AGRs are combined with GAS. Experiments on various real-world datasets verify the efficacy of our proposed GAS. The implementation code is provided in https://github.com/YuchenLiu-a/byzantine-gas

LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communications

The Controller Area Network (CAN) is considered as the de-facto standard for the in-vehicle communications due to its real-time performance and high reliability. Unfortunately, the lack of security protection on the CAN bus gives attackers the opportunity to remotely compromise a vehicle. In this paper, we propose a Lightweight Encryption and Authentication Protocol (LEAP) with low cost and high efficiency to address the security issue of the CAN bus. LEAP exploits the security-enhanced stream cipher primitive to provide encryption and authentication for the CAN messages. Compared with the state-of-the-art Message Authentication Code (MAC) based approaches, LEAP requires less memory, is 8X faster, and thwarts the most recently proposed attacks.Comment: 7 pages, 9 figures, 3 table

Optimization and process effect for microalgae carbon dioxide fixation technology applications based on carbon capture: a comprehensive review

Microalgae carbon dioxide (CO2) fixation technology is among the effective ways of environmental protection and resource utilization, which can be combined with treatment of wastewater and flue gas, preparation of biofuels and other technologies, with high economic benefits. However, in industrial application, microalgae still have problems such as poor photosynthetic efficiency, high input cost and large capital investment. The technology of microalgae energy development and resource utilization needs to be further studied. Therefore, this work reviewed the mechanism of CO2 fixation in microalgae. Improving the carbon sequestration capacity of microalgae by adjusting the parameters of their growth conditions (e.g., light, temperature, pH, nutrient elements, and CO2 concentration) was briefly discussed. The strategies of random mutagenesis, adaptive laboratory evolution and genetic engineering were evaluated to screen microalgae with a high growth rate, strong tolerance, high CO2 fixation efficiency and biomass. In addition, in order to better realize the industrialization of microalgae CO2 fixation technology, the feasibility of combining flue gas and wastewater treatment and utilizing high-value-added products was analyzed. Considering the current challenges of microalgae CO2 fixation technology, the application of microalgae CO2 fixation technology in the above aspects is expected to establish a more optimized mechanism of microalgae carbon sequestration in the future. At the same time, it provides a solid foundation and a favorable basis for fully implementing sustainable development, steadily promoting the carbon peak and carbon neutrality, and realizing clean, green, low-carbon and efficient utilization of energy

Physics-based Compact Model of Integrated Gate-Commutated Thyristor with Multiple Effects for High Power Application

This paper presents a physics-based compact model of integrated gate-commutated thyristor (IGCT) with multiple effects for high power application. The proposed model has both acceptable accuracy and computation time requirement, which is suitable for system level circuit simulation and IGCT’s whole wafer modelling work. First, the development of IGCT model is discussed and the one-dimension phenomenon of IGCT is analyzed in the paper. Second, a physics-based compact model of IGCT is proposed. The proposed model of IGCT includes multiple physical effects that are crucial to IGCTs working in high power applications. These physical effects include the impact ionization effect, moving boundary of depletion region during punch-thourgh (PT) and the local lifetime region. The Fourier series solution is applied for the ambipolar diffusion equation in the base region. Third, the proposed model is implemented in Simulink and compared with the model in Silvaco Atlas, a finite-element (FEM) tool. Finally, the proposed compact model of IGCT is validated by experiments