Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters

Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enhancing the robustness, the clean accuracy will decline to a certain extent, implying a trade-off existed between the accuracy and robustness. In this paper, we firstly empirically find an obvious distinction between standard and robust models in the filters' weight distribution of the same architecture, and then theoretically explain this phenomenon in terms of the gradient regularization, which shows this difference is an intrinsic property for DNNs, and thus a static network architecture is difficult to improve the accuracy and robustness at the same time. Secondly, based on this observation, we propose a sample-wise dynamic network architecture named Adversarial Weight-Varied Network (AW-Net), which focuses on dealing with clean and adversarial examples with a ``divide and rule" weight strategy. The AW-Net dynamically adjusts network's weights based on regulation signals generated by an adversarial detector, which is directly influenced by the input sample. Benefiting from the dynamic network architecture, clean and adversarial examples can be processed with different network weights, which provides the potentiality to enhance the accuracy and robustness simultaneously. A series of experiments demonstrate that our AW-Net is architecture-friendly to handle both clean and adversarial examples and can achieve better trade-off performance than state-of-the-art robust models

C2\mathbf{C}^2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection

Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to facilitate robust detection for around-the-clock applications, has received extensive attention in recent years. With the help of IR images, object detectors have been more reliable and robust in practical applications by using RGB-IR combined information. However, existing methods still suffer from modality miscalibration and fusion imprecision problems. Since transformer has the powerful capability to model the pairwise correlations between different features, in this paper, we propose a novel Calibrated and Complementary Transformer called $\mathrm{C}^2$Former to address these two problems simultaneously. In $\mathrm{C}^2$Former, we design an Inter-modality Cross-Attention (ICA) module to obtain the calibrated and complementary features by learning the cross-attention relationship between the RGB and IR modality. To reduce the computational cost caused by computing the global attention in ICA, an Adaptive Feature Sampling (AFS) module is introduced to decrease the dimension of feature maps. Because $\mathrm{C}^2$Former performs in the feature domain, it can be embedded into existed RGB-IR object detectors via the backbone network. Thus, one single-stage and one two-stage object detector both incorporating our $\mathrm{C}^2$Former are constructed to evaluate its effectiveness and versatility. With extensive experiments on the DroneVehicle and KAIST RGB-IR datasets, we verify that our method can fully utilize the RGB-IR complementary information and achieve robust detection results. The code is available at https://github.com/yuanmaoxun/Calibrated-and-Complementary-Transformer-for-RGB-Infrared-Object-Detection.git

Boosting Adversarial Transferability with Learnable Patch-wise Masks

Adversarial examples have raised widespread attention in security-critical applications because of their transferability across different models. Although many methods have been proposed to boost adversarial transferability, a gap still exists in the practical demand. In this paper, we argue that the model-specific discriminative regions are a key factor to cause the over-fitting to the source model, and thus reduce the transferability to the target model. For that, a patch-wise mask is utilized to prune the model-specific regions when calculating adversarial perturbations. To accurately localize these regions, we present a learnable approach to optimize the mask automatically. Specifically, we simulate the target models in our framework, and adjust the patch-wise mask according to the feedback of simulated models. To improve the efficiency, Differential Evolutionary (DE) algorithm is utilized to search for patch-wise masks for a specific image. During iterative attacks, the learned masks are applied to the image to drop out the patches related to model-specific regions, thus making the gradients more generic and improving the adversarial transferability. The proposed approach is a pre-processing method and can be integrated with existing gradient-based methods to further boost the transfer attack success rate. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our method. We incorporate the proposed approach with existing methods in the ensemble attacks and achieve an average success rate of 93.01% against seven advanced defense methods, which can effectively enhance the state-of-the-art transfer-based attack performance

Parallel experimental study of a novel super-thin thermal absorber based photovoltaic/thermal (PV/T) system against conventional photovoltaic (PV) system

Photovoltaic (PV) semiconductor degrades in performance due to temperature rise. A super thin-conductive thermal absorber is therefore developed to regulate the PV working temperature by retrofitting the existing PV panel into the photovoltaic/thermal (PV/T) panel. This article presented the parallel comparative investigation of the two different systems through both laboratory and field experiments. The laboratory evaluation consisted of one PV panel and one PV/T panel respectively while the overall field system involved 15 stand-alone PV panels and 15 retrofitted PV/T panels. The laboratory testing results demonstrated the PV/T panel could achieve the electrical efficiency of about 16.8% (relatively 5% improvement comparing with the stand-alone PV panel), and yield an extra amount of heat with thermal efficiency of nearly 65%. The field testing results indicated that the hybrid PV/T panel could enhance the electrical return of PV panels by nearly 3.5%, and increase the overall energy output by nearly 324.3%. Further opportunities and challenges were then discussed from aspects of different PV/T stakeholders to accelerate the development. It is expected that such technology could become a significant solution to yield more electricity, offset heating load freely and reduce carbon footprint in contemporary energy environment

Theoretical investigation of the thermal performance of a novel solar loop-heat-pipe façade-based heat pump water heating system

The aim of the paper was to present a dedicated theoretical investigation into the thermal performance of a novel solar loop-heat-pipe façade based heat pump water heating system. This involved thermo-fluid analyses, computer numerical model development, the model running up, modelling result analyses and conclusion. An energy balance network was established on each part and the whole range of the system to address the associated energy conversion and transfer processes. On basis of this, a computer numerical model was developed and run up to predict the thermal performance of such a system at different system configurations, layouts and operational conditions. It was suggested that the loop heat pipes could be filled with either water, R134a, R22 or R600a; of which R600a is the favourite working fluid owing to its relatively larger heat transfer capacity and positive pressure in operation. Variations in the system configuration, i.e., glazing covers, heat exchangers, would lead to identifiable differences in the thermal performance of the system, represented by the thermal efficiency and COP. Furthermore, impact of the external operational parameters, i.e., solar radiation and ambient air temperature, to the system's thermal performance was also investigated. The research was based on an innovative loop-heat-pipe façade and came up with useful results reflecting the thermal performance of the combined system between the façade and heat pump. This would help promote development and market penetration of such an innovative solar heating technology, and thus contribute to achieving the global targets in energy saving and carbon emission reduction