Brain disorders are associated with impairments in cognitive and social functioning, placing a substantial burden on families, healthcare systems, and communities. However, accurate diagnosis remains challenging due to complex higher order interactions among brain regions. Existing graph-based methods are largely limited to pairwise connectivity. In addition, these methods often fail to fully exploit well-established neuroscientific prior knowledge, resulting in limited biological interpretability and suboptimal diagnostic performance. Therefore, we propose a prior knowledge-guided adaptive hypergraph learning (PK-AHGL) framework that represents individual-level functional connectivity networks as hypergraphs to capture higher order multiregion interactions while incorporating neuroscientific prior knowledge for brain disorder diagnosis. PK-AHGL consists of three key modules: 1) an adaptive hypergraph convolution module. Unlike traditional hypergraph neural networks that use static hyperedge weights, this module adaptively learns the weights of different hyperedges; 2) a sparse affinity Laplacian module. Key brain regions are extracted from disorder related functional brain networks and used as prior knowledge. Based on these regions, we compute a hyperedge similarity matrix that encourages similar hyperedges to have similar weights; and 3) a proportional margin ranking module. This module further utilizes prior knowledge by guiding hyperedges containing a higher proportion of key brain regions to obtain larger weights. Experiments on autism brain imaging data exchange (ABIDE), Strategic Research Program for the Promotion of Brain Science (SRPBS)-schizophrenia (SCZ), SRPBS-major depressive disorder (MDD), and Alzheimer’s disease neuroimaging initiative (ADNI) show that PK-AHGL achieves accuracies of 75.78%, 82.66%, 74.89%, and 77.22%, respectively, outperforming multiple state-of-the-art methods. These results suggest that PK-AHGL provides an effective auxiliary tool for brain disorder diagnosis and may support community-oriented mental health services.

Backdoor attacks pose significant security risks to deep neural networks (DNNs). These attacks involve models that make intentionally incorrect (and potentially targeted) predictions on poisoned inputs containing carefully crafted triggers, while operating normally with clean inputs. Prior studies have investigated the invisibility of backdoor triggers to improve attack stealthiness. However, they primarily concentrate on achieving invisibility solely in the spatial domain, ignoring the generation of invisible triggers in the frequency and feature domains. This constraint makes the poisoned images vulnerable to detection by recent defense mechanisms. To tackle this problem, we introduce a Triple stealthy BAckdoor attack approach, termed TriBA, which simultaneously ensures the invisibility of triggers in all the spatial, frequency, and feature domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we initially utilize Wavelet Transform to embed the high-frequency information from the trigger image into the clean image to ensure effective attack performance. Then, to achieve strong stealthiness across both spatial and frequency domains, we integrate Fourier Transform and Cosine Transform to blend the poisoned image and clean image in the frequency domain. Furthermore, TriBA adopts an attack strategy to make the backdoor features similar to clean features in the feature space, which guarantees trigger invisibility in the feature domain while maintaining attack effectiveness. We theoretically prove the effectiveness of this strategy. Finally, TriBA has been comprehensively evaluated on four datasets against popular image classifiers, demonstrating a marked improvement over existing state-of-the-art backdoor attacks in terms of both attack success rate and stealthiness.

Pathological diagnosis assists in saving human lives, but such models are annotation hungry and pathological images are notably expensive to annotate. Contrastive learning could be a promising solution that relies only on the unlabeled training data to generate informative representations. However, the majority of current methods in contrastive learning have the following two issues: (1) positive samples produced through random augmentation are less challenging, and (2) false negative pairs problem caused by negative sampling bias. To alleviate the above issues, we propose a novel contrastive learning method called Cluster-Aware Adversarial Contrastive Learning (CA2CL). Specifically, a mixed data augmentation technique is provided to learn more transferable representations by generating more discriminative sample pairs. Furthermore, to mitigate the effects of inherent false negative pairs, we adopt a cluster-aware loss to identify similarities between instances and incorporate them into the process of contrastive learning. Finally, we generate challenging contrastive data pairs by adversarial learning, and adversarially learn robust representations in the representation space without the labeled training data, which aims to maximize the similarity between the augmented sample and the related adversarial sample. Our proposed CA2CL is evaluated on two public datasets: NCT-CRC-HE and PCam for the fine-tuning and linear evaluation tasks and on two other public datasets: GlaS and CARG for the detection and segmentation tasks, respectively. Extensive experimental results demonstrate the superior performance improvement of our method over several Self-supervised learning (SSL) methods and ImageNet pretraining particularly in scenarios with limited data availability for all four tasks.

Adversarial attacks have challenged the security of deep neural networks (DNNs) recently. The most prominent adversarial attack methods include backdoor attacks, adversarial examples, etc. These attack methods inject triggers or perturbations into images, leading to extremely dangerous security vulnerability in deep learning domain. The various forms of adversarial attacks can contaminate DNNs with their distinct characteristics. The complexity of adversarial attack poses a great challenge to designing a general defense strategy. In this paper, we propose a novel defense method against most of adversarial attacks through Image Decomposition and Reconstruction (IDR). Our method can be applied to poisoned images without the need for internal information about the model or any prior knowledge of the clean/poisoned images. We apply a linear transformation on the poisoned image to destroy the perturbations or triggers and deploy a pre-trained diffusion model to reconstruct the original information. In particular, we propose a novel reverse process that utilizes the consistency of range-null space decomposition to guide the generation of purified images. The decomposition of the range-null space can guarantee the retrieval of image information, which enhances the robustness of our method and contributes to the reliable purification of poisoned images. We assess the effectiveness of our proposed IDR against various prevalent backdoor attacks, adversarial examples and Image-Scaling attack methods. The experimental results highlight the outstanding defensive capabilities of our proposed IDR, demonstrating an exceptionally high defense success rate.

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs containing well-designed triggers, while behaving normally on clean inputs. Prior researches have explored the invisibility of backdoor triggers to enhance attack stealthiness. However, most of them only focus on the invisibility in the spatial domain, neglecting the generation of invisible triggers in the frequency domain. This limitation renders the generated poisoned images easily detectable by recent defense methods. To address this issue, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, DUBA adopts a novel attack strategy, training the model with weak triggers and attacking with strong triggers to further enhance attack performance and stealthiness. DUBA is evaluated extensively on four datasets against popular image classifiers, showing significant superiority over state-of-the-art backdoor attacks in attack success rate and stealthiness.

Backdoor defense is crucial to ensure the safety and robustness of machine learning models when under attack. However, most existing methods specialize in either the detection or removal of backdoors, but seldom both. While few works have addressed both, these methods rely on strong assumptions or entail significant overhead costs, such as the need of task-specific samples for detection and model retraining for removal. Hence, the key challenge is how to reduce overhead and relax unrealistic assumptions. In this work, we propose two Energy-Based BAckdoor defense methods, called EBBA and EBBA+, that can achieve both backdoored model detection and backdoor removal with low overhead. Our contributions are twofold: First, we offer theoretical analysis for our observation that a predefined target label is more likely to occur among the top results for various samples. Inspired by this, we develop an enhanced energy-based technique, called EBBA, to detect backdoored models without task-specific samples (i.e., samples from any tasks). Secondly, we theoretically analyze that after data corruption, the original clean label of a poisoned sample is more likely to be predicted as a top output by the model, a sharp contrast to clean samples. Accordingly, we extend EBBA to develop EBBA+, a new transferred energy approach to efficiently detect poisoned images and remove backdoors without model retraining. Extensive experiments on multiple benchmark datasets demonstrate the superior performance of our methods over baselines in both backdoor detection and removal. Notably, the proposed methods can effectively detect backdoored model and poisoned images as well as remove backdoors at the same time.

Abstract Deep learning has been widely used in many applications such as face recognition, autonomous driving, etc. However, deep learning models are vulnerable to various adversarial attacks, among which backdoor attack is emerging recently. Most of the existing backdoor attacks use the same trigger or the same trigger generation approach to generate the poisoned samples in the training and testing sets, which is also commonly adopted by many backdoor defense strategies. In this paper, we develop an enhanced backdoor attack (EBA) that aims to reveal the potential flaws of existing backdoor defense methods. We use a low-intensity trigger to embed the backdoor, while a high-intensity trigger to activate it. Furthermore, we propose an enhanced coalescence backdoor attack (ECBA) where multiple low-intensity incipient triggers are designed to train the backdoor model, and then, all incipient triggers are gathered on one sample and enhanced to launch the attack. Experiment results on three popular datasets show that our proposed attacks can achieve high attack success rates while maintaining the model classification accuracy of benign samples. Meanwhile, by hiding the incipient poisoned samples and preventing them from activating the backdoor, the proposed attack exhibits significant stealth and the ability to evade mainstream defense methods during the model training phase.

In recent years, wireless rechargeable sensor networks (WRSNs), as a crucial technology in cyber–physical–social systems (CPSSs), have gradually become a hotspot of research, with the development of wireless energy transmission technology. In previous works, the objective is to maximize the survival rate of sensor nodes. However, in this article, we focus on maintaining more targets. First, it details the charging scheduling problem of maximizing coverage of targets (CoT) in on-demand charging architecture of WRSNs. Also, the problem is formalized as a multiple-objective optimization problem, which aims at maximizing the CoT and the energy efficiency simultaneously. After that, the charging scheduling for maximizing coverage of targets (CSCT) scheme is proposed to achieve the above objectives. Then, the problem is reformulated as a Deadline-TSP problem that is NP-hard. To address this problem, we design an energy predictive model and propose the CSCT with an$n$-path ($n$-CSCT) scheme that has an$O(|\mathcal{N}|^n)$computational complexity. In addition, the resurrection of sensor nodes is considered in this article. Thus, the$n$-CSCT with node resurrection ($n$-CSCT-R) scheme is proposed for this case. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

The increasing prevalence of deep neural networks (DNNs) in industrial artificial intelligence systems (IAISs) promotes the development of industrial automation. However, the growing employment of DNNs also exposes them to various attacks. Recent studies have shown that the data preprocessing process of DNNs is vulnerable to image-scaling attack. Such attacks can craft an attack image, which looks like a given source image but becomes a different target image after being scaled to the target size. The attack images generated by existing image-scaling attacks are easily perceivable to the human visual system, significantly degrading the attack's stealthiness. In this paper, we investigate image-scaling attack from the perspective of signal processing. We unearth that the root cause of the weak deceiving effects of existing image-scaling attack images lies in the introduction of additional high-frequency signals during their construction. Thus, we propose an enhanced image-scaling attack (EIS), which employs adversarial images crafted based on the source (“clean”) images as the target images. Those adversarial images preserve the “clean” pixel information of source images, thereby significantly mitigating the emergence of additional high-frequency signals in the attack images. Specifically, we consider three realistic threat models covering deep models' training and inference phases. Correspondingly, we design three strategies tailored to generate adversarial images with vicious patterns. These patterns are subsequently integrated into the attack images, which can mislead a model with target input size after the necessary scaling operation. Extensive experiments validate the superior performance of the proposed image-scaling attack compared to the original one.

Recent years have witnessed extensive applications of Deep Neural Networks (DNNs) in various vision tasks. However, DNNs are vulnerable to adversarial images crafted by introducing perturbations into inputs to induce incorrect predictions. Unlike$L_{p}$-norm restricted adversarial attacks, many unrestricted attacks have been proposed by modifying attributes of the image (e.g., edge, color), while the critical components of the image are preserved. However, most existing unrestricted attacks easily introduce unnatural distortions, colors, stains and schemes, in the generated adversarial images. This paper proposes a novel unrestricted attack (named AdvST) to create stylized, natural-looking, and high-transferability adversarial images. The basic idea of AdvST is to embed adversarial perturbations when transferring the style from the reference image onto the original image (i.e., rendering the original image's semantic contents into the reference image's style). To further improve the image quality of generated adversarial images, we refine two kinds of reference images (i.e., photographs and artworks) based on different attractive styles and design two attacks accordingly. For photorealistic attack, we incorporate semantic information obtained from segmentation maps to improve the photo realism of adversarial images. For artistic attack, we propose integrating edge information extracted by the Laplace operator to preserve the structural integrity of the original image. Extensive experimental results validate the superior performance of AdvST in terms of adversarial image quality and black-box transferability compared to benchmark methods.

Self-supervised learning provides a possible solution to extract effective visual representations from unlabeled pathological images. However, most of the existing methods either do not effectively utilize domain-specific information or are designed and optimized for image classification, resulting in these pre-trained models that may not be optimal for pathological image segmentation. In this paper, we propose DKSL: Domain-specific Knowledge guided Self-supervised Learning, which uses image reconstruction tasks to aid contrastive learning and exploits single-dye stained pathological images after stain separation as domain-specific knowledge to guide the model. Our method provides a novel way to exploit the domain-specific knowledge of pathological images. In contrastive learning, we add single-dye stained images as an expansion of the original positive samples to the contrastive learning process to preserve more global semantic information. In image reconstruction, the model is forced to focus on local image details relevant to downstream tasks by reconstructing single-dye stained images from the representation extracted by the encoder of contrastive learning. Finally, the encoder and decoder from the pre-training stage are fine-tuned by the downstream segmentation task. Fine-tuning experimental results demonstrate that DKSL outperforms state-of-the-art methods with Dices of 90.50% and 79.68% on two publicly available datasets, GlaS and MoNuSeg, respectively.

Accurately predicting survival of esophageal cancer is essential for clinical precision treatment. However, the existing region of interest (ROI) based methods not only require prior medical knowledge to complete the delineation of tumor, but may also lead to excessive sensitivity of the model towards ROI. To address these challenges, we design a fully automated CT-guided learning that combines a CNN-Transformer size aware U-Net and a ranked survival prediction network together to automatically predict the survival of patients with esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism into the base of the encoder in the U-Net to capture the long-range dependency in the 3D CT images. Then, to alleviate the imbalance between the ROI and the background in CT images, we design a size-aware coefficient for the segmentation loss. Finally, we design a ranked pair sorting loss to learn more fully the ranked information hidden in esophageal cancer patients. To validate the effectiveness of our method, we conduct extensive experiments on a dataset containing 759 esophageal cancer samples. The experimental results demonstrate that our proposed method can still achieve the best performance in survival prediction without ROI ground truth.

Deep neural networks (DNNs) are gradually becoming the preference for the various vision applications of smart cities. However, their success heavily relies on the access to extensive training data and substantial computational resources, posing challenges in training large-scale models for diverse smart city applications. Consequently, the third-party services and resources are often utilized to train the models, exposing them to the potential backdoor attacks. Despite the escalating threat of such attacks, many existing strategies primarily focus on enhancing the stealthiness and evading defenses, often neglecting practical feasibility in the real-world scenarios. In this paper, we introduce a novel backdoor attack named bokeh based backdoor attack $(B^{3}A)$, which leverages the bokeh effect as the trigger. Once the backdoor is deployed in a vision application model, the model’s malicious behavior can be activated solely by using the captured bokeh images. Specifically, we employ saliency and depth estimation maps to synthesize the bokeh images, effectively serving as the poisoned samples. Moreover, we devise a reference model to impose constraints on the feature representations of the poisoned images, thereby further enhancing their stealthiness in the feature space. Extensive experiments demonstrate the attack effects of $B^{3}A$, even on the bokeh photos taken from Digital Still Cameras (DSC) and smartphones.

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19.

Radio frequency identification (RFID) technology has been deployed in various scenarios in recent years. In some practical RFID applications, the items attached with tags can be divided into multiple groups. Thus, the efficient and accurate missing tag detection of each group is critical. Accordingly, this article concentrates on the problem of missing tag detection in the multiple-group RFID systems, after which three distinctive protocols are proposed. First, we propose an aptitudinal multiple-group missing tag detection protocol, which makes full use of the expected singleton slots. Then, an enhanced multiple-group missing tag detection protocol is proposed, which can achieve significant broadcast and response savings. Finally, an accurate and expeditious multiple-group missing tag detection protocol is designed, the detection reliability of which can approximate 100%. The theoretical analysis and extensive simulations are conducted and the results verify that the proposed protocols in this article outperform the other ones.

The cause of Alzheimer's disease (AD) is insufficient to understand so far, and its diagnosis is challenging in clinical practice. Recently, the convolutional neural network (CNN) model has shown impressive performance in medical image analysis. Combining CNN with magnetic resonance imaging (MRI) image has excellent potential for AD diagnosis. However, it is still a challenging task. To address the challenge, we propose a joint learning method based on multi-scale representation (JL-MSR). The multi-scale representation is proposed to obtain more feature maps by the multi-scale atrous convolutions. Furthermore, in order to use the intrinsic relationship between diagnostic results and clinical scores, we propose a joint learning strategy using the diagnosis result as the primary label and the Mini-Mental State Examination (MMSE) score as the secondary label to joint training. The proposed method is evaluated on a dataset of 417 subjects (including 188 AD and 229 health controls (HC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method achieves an accuracy of 88.1% and an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.942 for AD diagnosis, respectively. Compared with a state-of-the-art method in AD diagnosis, our proposed method performs better, and has potential in clinical diagnosis.

There have been many reliable routing algorithms for wired networks. For routing in wireless sensor networks, the reliability aspect has not been paid as much attention. GEM (Graph EMbedding for sensor networks) is an innovative routing algorithm for wireless sensor networks that is based on the idea of graph embedding. However, it cannot survive edge failures well. In this paper, we propose GW-GEM (Greedy-Walk GEM), a GEM-based multi-path routing algorithm that preserves the advantages of GEM and improves its reliability performance significantly. Specifically, in the case where 1% of edges fail in a 900-node simulated network, GEM leads to a path error rate of 9.2% while GW-GEM only results in a path error rate of 1%.