Currently, researchers think that the inherent robustness of spiking neural networks (SNNs) stems from their biologically plausible spiking neurons, and are dedicated to developing more bio-inspired models to defend attacks. However, most work relies solely on experimental analysis and lacks theoretical support, and the direct-encoding method and fixed membrane potential leak factor they used in spiking neurons are simplified simulations of those in the biological nervous system, which makes it difficult to ensure generalizability across all datasets and networks. Contrarily, the biological nervous system can stay reliable even in a highly complex noise environment, one of the reasons is selective visual attention and non-fixed membrane potential leaks in biological neurons. This biological finding has inspired us to design a highly robust SNN model that closely mimics the biological nervous system. In our study, we first present a unified theoretical framework for SNN robustness constraint, which suggests that improving the encoding method and evolution of the membrane potential leak factor in spiking neurons can improve SNN robustness. Subsequently, we propose a robust SNN (FEEL-SNN) with Frequency Encoding (FE) and Evolutionary Leak factor (EL) to defend against different noises, mimicking the selective visual attention mechanism and non-fixed leak observed in biological systems. Experimental results confirm the efficacy of both our FE, EL, and FEEL methods, either in isolation or in conjunction with established robust enhancement algorithms, for enhancing the robustness of SNNs.

Reshaping, a point operation that alters the characteristics of signals, has been shown capable of improving the compression ratio in video coding practices. Out-of-loop reshaping that directly modifies the input video signal was first adopted as the supplemental enhancement information (SEI) for the HEVC/H.265 without the need to alter the core design of the video codec. VVC/H.266 further improves the coding efficiency by adopting in-loop reshaping that modifies the residual signal being processed in the hybrid coding loop. In this paper, we theoretically analyze the rate-distortion performance of the in-loop reshaping and use experiments to verify the theoretical result. We prove that the in-loop reshaping can improve coding efficiency when the entropy coder adopted in the coding pipeline is suboptimal, which is in line with the practical scenarios that video codecs operate in. We derive the PSNR gain in a closed form and show that the theoretically predicted gain is consistent with that measured from experiments using standard testing video sequences.

Adversarial training (AT) has shown excellent high performance in defending against adversarial examples. Recent studies demonstrate that examples are not equally important to the final robustness of models during AT, that is, the so-called hard examples that can be attacked easily exhibit more influence than robust examples on the final robustness. Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model. However, defining effective heuristics to search for hard examples is still difficult. In this article, inspired by the information bottleneck (IB) principle, we uncover that an example with high mutual information of the input and its associated latent representation is more likely to be attacked. Based on this observation, we propose a novel and effective adversarial training method (InfoAT). InfoAT is encouraged to find examples with high mutual information and exploit them efficiently to improve the final robustness of models. Experimental results show that InfoAT achieves the best robustness among different datasets and models in comparison with several state-of-the-art methods.

Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature. The aim of IM, which is NP-hard, is to select a set of$k$users known as seed users who can influence the most individuals in the social network. The state-of-the-art algorithms estimate the expected influence of nodes based on sampled diffusion paths. As the number of required samples has been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and the efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency. In this article, we present an orthogonal and novel paradigm to address the IM problem by leveraging deep reinforcement learning (RL) to estimate the expected influence. In particular, we present a novel framework called deeP reInforcement leArning-based iNfluence maximizatiOn (PIANO) that incorporates network embedding and RL techniques to address this problem. In order to make it practical, we further present PIANO-E and PIANO$\text{@}\langle d\rangle $, both of which can be applied directly to answer IM without training the model from scratch. Experimental study on real-world networks demonstrates that PIANO achieves the best performance with respect to efficiency and influence spread quality compared to state-of-the-art classical solutions. We also demonstrate that the learned parametric models generalize well across different networks. Besides, we provide a pool of pretrained PIANO models such that any IM task can be addressed by directly applying a model from the pool without training over the targeted network.

The massive growth in interconnected devices from a multiplicity of networks goes hand-in-hand with the emergence of the Internet of Things (IoT) paradigm. As a critical component of the IoT, sensor networks have become ubiquitous and widely used in various application domains. However, open-ended wireless communication brings severe threats to user privacy and security. Attackers from outside the network can trace back along the data stream to capture the source node, which poses a significant threat to the privacy of the data source. A feasible defense method is to interfere with the attacker’s tracking process through forged data streams. However, the related traditional solutions generally have shortcomings in terms of balancing security and efficiency. Therefore, this article proposes a pseudopacket scheduling algorithm (PPSA), which aims at reasonably regulating the process of pseudopacket generation to interfere with the adversary’s tracking to the data source. The algorithm comprises three phases. First, the sink node performs geographic information acquisition and neighbor node discovery with a flood-based method. Then, the sink node uses a self-adapting proxy selection method to construct backbone routes with both randomness and low latency to receive actual packets. Finally, the nodes on both sides of the backbone routes follow a pseudopacket scheduling strategy to interfere with the adversary’s tracking of the source locations. The experimental results showcase that our proposed scheme effectively controls the additional energy consumption and transmission delays within acceptable ranges while ensuring adequate location privacy.

Deep neural networks are discovered to be non-robust when attacked by imperceptible adversarial examples, which is dangerous for it applied into medical diagnostic system that requires high reliability. However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks. The pre-processing methods (e.g., random resizing, compression) may lead to the loss of the small lesions feature in the medical image. Retraining the network on the augmented data set is also not practical for medical models that have already been deployed online. Accordingly, it is necessary to design an easy-to-deploy and effective defense framework for medical diagnostic tasks. In this paper, we propose a Robust and Retrain-Less Diagnostic Framework for Medical pretrained models against adversarial attack (i.e., MedRDF). It acts on the inference time of the pretrained medical model. Specifically, for each test image, MedRDF firstly creates a large number of noisy copies of it, and obtains the output labels of these copies from the pretrained medical diagnostic model. Then, based on the labels of these copies, MedRDF outputs the final robust diagnostic result by majority voting. In addition to the diagnostic result, MedRDF produces the Robust Metric (RM) as the confidence of the result. Therefore, it is convenient and reliable to utilize MedRDF to convert pretrained non-robust diagnostic models into robust ones. The experimental results on COVID-19 and DermaMNIST datasets verify the effectiveness of our MedRDF in improving the robustness of medical diagnostic models.

To improve the coding performance of depth maps, 3D-HEVC includes several new depth intra coding tools at the expense of increased complexity due to a flexible quadtree Coding Unit/Prediction Unit (CU/PU) partitioning structure and a huge number of intra mode candidates. Compared to natural images, depth maps contain large plain regions surrounded by sharp edges at the object boundaries. Our observation finds that the features proposed in the literature either speed up the CU/PU size decision or intra mode decision and they are also difficult to make proper predictions for CUs/PUs with the multi-directional edges in depth maps. In this work, we reveal that the CUs with multi-directional edges are highly correlated with the distribution of corner points (CPs) in the depth map. CP is proposed as a good feature that can guide to split the CUs with multi-directional edges into smaller units until only single directional edge remains. This smaller unit can then be well predicted by the conventional intra mode. Besides, a fast intra mode decision is also proposed for non-CP PUs, which prunes the conventional HEVC intra modes, skips the depth modeling mode decision, and early determines segment-wise depth coding. Furthermore, a two-step adaptive corner point selection technique is designed to make the proposed algorithm adaptive to frame content and quantization parameters, with the capability of providing the flexible tradeoff between the synthesized view quality and complexity. Simulation results show that the proposed algorithm can provide about 66% time reduction of the 3D-HEVC intra encoder without incurring noticeable performance degradation for synthesized views and it also outperforms the previous state-of-the-art algorithms in term of time reduction and ∆ BDBR.

Image segmentation is an important issue in computer vision. The methods based on Fuzzy C-Means (FCM) algorithms have gained success. However, these approaches deal with each pixel as a separate object, which will ignore the spatial information among these pixels. This paper proposes an approach which combines the Fuzzy C-Means algorithm and Graph Cut Theory both for gray and color image segmentation. We adopt the Turbopixel algorithm to split the color image into varied small regions called superpixels for presegmentation and extract color histogram features from the superpixels. Based on color histogram feature, we use FCM to make the original clusters. Then we build a graph model, and use maximum flow algorithm to get the minimum cut, namely the initial segmentation result of the image. Finally, we use a recursive process to achieve the result of image segmentation. The key point of our approach is building a great graphical model and utilizing the existing binary segmentation model to solve the multi-value segmentation. Experimental results show that our approach can obtain good segmentation results comparing with FCM only under different parameters setup and binary segmentation.