Weighted-Sampling Audio Adversarial Example Attack

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd

Launching a Robust Backdoor Attack under Capability Constrained Scenarios

As deep neural networks continue to be used in critical domains, concerns over their security have emerged. Deep learning models are vulnerable to backdoor attacks due to the lack of transparency. A poisoned backdoor model may perform normally in routine environments, but exhibit malicious behavior when the input contains a trigger. Current research on backdoor attacks focuses on improving the stealthiness of triggers, and most approaches require strong attacker capabilities, such as knowledge of the model structure or control over the training process. These attacks are impractical since in most cases the attacker's capabilities are limited. Additionally, the issue of model robustness has not received adequate attention. For instance, model distillation is commonly used to streamline model size as the number of parameters grows exponentially, and most of previous backdoor attacks failed after model distillation; the image augmentation operations can destroy the trigger and thus disable the backdoor. This study explores the implementation of black-box backdoor attacks within capability constraints. An attacker can carry out such attacks by acting as either an image annotator or an image provider, without involvement in the training process or knowledge of the target model's structure. Through the design of a backdoor trigger, our attack remains effective after model distillation and image augmentation, making it more threatening and practical. Our experimental results demonstrate that our method achieves a high attack success rate in black-box scenarios and evades state-of-the-art backdoor defenses.Comment: 9 pages, 6 figure

Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing

The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists

Effects of Methotrexate on Plasma Cytokines and Cardiac Remodeling and Function in Postmyocarditis Rats

Excessive immune activation and inflammatory mediators may play a critical role in the pathogenesis of chronic heart failure. Methotrexate is a commonly used anti-inflammatory and immunosuppressive drug. In this study, we used a rat model of cardiac myosin-induced experimental autoimmune myocarditis to investigate the effects of low-dose methotrexate (0.1 mg/kg/d for 30 d) on the plasma level of cytokines and cardiac remodeling and function. Our study showed that levels of tumor necrosis factor-(TNF-)alpha and interleukin-6 (IL-6) are significantly increased in postmyocarditis rats, compared with the control rats. Methotrexate treatment reduced the plasma levels of TNF-alpha and IL-6 and increased IL-10 level, compared to saline treatment. In addition, postmyocarditis rats showed significant cardiac fibrosis characterized by increased myocardial collagen volume fraction, perivascular collagen area, and the ratio of collagen type I to type III, compared with the control rats. However, MTX treatment not only markedly attenuated cardiac fibrosis, diminished the left ventricular end-diastolic dimension, but also increased the left ventricular ejection fraction and fractional shortening. Collectively, these results suggest that low-dose methotrexate has ability to regulate inflammatory responses and improves cardiac function and hence contributes to prevent the development of postmyocarditis dilated cardiomyopathy