CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many AI fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs. Researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufficient robustness and low transferability. We propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and high-transferability adversarial attack. First, as a type of generative model-based attack, CAG shows significant speedup (at least 500 times) in generating adversarial examples compared to the state-of-the-art attacks such as PGD and C\&W. CAG only needs a single generative model to perform targeted attack to any targeted class. Because CAG encodes the label information into a trainable embedding layer, it differs from prior generative model-based adversarial attacks that use $n$ different copies of generative models for $n$ different targeted classes. As a result, CAG significantly reduces the required memory cost for generating adversarial examples. CAG can generate adversarial perturbations that focus on the critical areas of input by integrating the class activation maps information in the training process, and hence improve the robustness of CAG attack against the state-of-art adversarial defenses. In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations. Extensive experiments on different datasets and DNN models have verified the real-time, low-cost, enhanced-robustness, and high-transferability benefits of CAG

Lycium barbarum polysaccharide attenuates cisplatininduced apoptosis in ovary granulosa cells via alleviation of endoplasmic reticulum stress

Purpose: To investigate the effect of Lycium barbarum polysaccharide (LBP) on apoptosis of ovary granulose cells (GCs), as well as its underlying mechanism.Methods: GCs were isolated from Sprague Dawley (SD) rats and divided into three groups: control group, model group (DDP) and LBP group. Cell morphology was observed by H & E staining under optical microscope. Expression of follicle stimulating hormone receptor (FSHR) was assessed by immunohistochemistry (IHC), while cell viability was assayed using 3-(4,5-dimethyl-2-thiazolyl)-2,5- diphenyl-2-H-tetrazolium bromide (MTT). Apoptosis was determined by flow cytometry. Expressions of glucose-regulated protein 78(GRP78), C/EBP homologous protein (CHOP), caspase-3, Bax protein and B cell lymphoma-2(Bcl-2) were assayed by Western blot and qRT-PCR.Results: Apoptosis index (37.6 ± 2.44 %) was significantly higher (p > 0.05) in DDP group than in the control group (14.3 ± 1.09 %), while mRNA levels and expressions of caspase-3, Bcl-2 and Bax increased significantly (p > 0.05). Expressions of GRP78 and CHOP in the DDP group were also higher than in the control group (p > 0.05). However, these effects were effectively blocked by co-incubation with LBP. Moreover, the DDP-induced increase in apoptosis index was dose-dependently and significantly lowered by LBP (p > 0.05).Conclusion: LBP exerts protective effect on cisplatin-induced apoptosis in ovary granulosa cells by alleviating endoplasmic reticulum stress and regulating levels of apoptosis-related proteins. Thus, LBP has the potential for alleviating adverse effects induced by cisplatin in the treatment of ovary granulosa lesions.Keywords: Cis-platin, Ovary granulosa lesions, Lycium barbarum polysaccharide, Endoplasmic reticulum stress, Apoptosi