HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible. Research on this problem is still in the embryonic stage and only a few methods are available. Nevertheless, existing methods rely on the complex heuristic algorithm or unreliable gradient estimation strategy, which probably fall into the local optimum and inevitably consume numerous queries, thus are difficult to craft satisfactory adversarial examples with high semantic similarity and low perturbation rate in a limited query budget. To alleviate above issues, we propose a simple yet effective framework to generate high quality textual adversarial examples under the black-box hard-label attack scenarios, named HQA-Attack. Specifically, after initializing an adversarial example randomly, HQA-attack first constantly substitutes original words back as many as possible, thus shrinking the perturbation rate. Then it leverages the synonym set of the remaining changed words to further optimize the adversarial example with the direction which can improve the semantic similarity and satisfy the adversarial condition simultaneously. In addition, during the optimizing procedure, it searches a transition synonym word for each changed word, thus avoiding traversing the whole synonym set and reducing the query number to some extent. Extensive experimental results on five text classification datasets, three natural language inference datasets and two real-world APIs have shown that the proposed HQA-Attack method outperforms other strong baselines significantly

Boosting Few-Shot Text Classification via Distribution Estimation

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.Comment: Accepted to AAAI 202

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors to facilitate score-based methods to obtain better results. However, these gradient priors still suffer from the edge gradient discrepancy issue and the successive iteration gradient direction issue, thus are difficult to simply extend to decision-based methods. In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. First, by leveraging the joint bilateral filter to deal with each random perturbation, DBA-GP can guarantee that the generated perturbations in edge locations are hardly smoothed, i.e., alleviating the edge gradient discrepancy, thus remaining the characteristics of the original image as much as possible. Second, by utilizing a new gradient updating strategy to automatically adjust the successive iteration gradient direction, DBA-GP can accelerate the convergence speed, thus improving the query efficiency. Extensive experiments have demonstrated that the proposed method outperforms other strong baselines significantly.Comment: Accepted by IJCAI 202

Gelatin-based anticancer drug delivery nanosystems: A mini review

Drug delivery nanosystems (DDnS) is widely developed recently. Gelatin is a high-potential biomaterial originated from natural resources for anticancer DDnS, which can effectively improve the utilization of anticancer drugs and reduce side effects. The hydrophilic, amphoteric behavior and sol-gel transition of gelatin can be used to fulfill various requirements of anticancer DDnS. Additionally, the high number of multifunctional groups on the surface of gelatin provides the possibility of crosslinking and further modifications. In this review, we focus on the properties of gelatin and briefly elaborate the correlation between the properties and anticancer DDnS. Furthermore, we discuss the applications of gelatin-based DDnS in various cancer treatments. Overall, we have summarized the excellent properties of gelatin and correlated with DDnS to provide a manual for the design of gelatin-based materials for DDnS

Flexible pressure sensors via engineering microstructures for wearable human-machine interaction and health monitoring applications

Flexible pressure sensors capable of transducing pressure stimuli into electrical signals have drawn extensive attention owing to their potential applications for human-machine interaction and healthcare monitoring. To meet these application demands, engineering microstructures in the pressure sensors are an efficient way to improve key sensing performances, such as sensitivity, linear sensing range, response time, hysteresis, and durability. In this review, we provide an overview of the recent advances in the fabrication and application of high-performance flexible pressure sensors via engineering microstructures. The implementation mechanisms and fabrication strategies of microstructures including micropatterned, porous, fiber-network, and multiple microstructures are systematically presented. The applications of flexible pressure sensors with microstructures in the fields of wearable human-machine interaction, and ex vivo and in vivo healthcare monitoring are comprehensively discussed. Finally, the outlook and challenges in the future improvement of flexible pressure sensors toward practical applications are presented

Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from hematoxylin and eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.Comment: 13 pages, 11 figures, 2table