Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin

Flavonoids from Lycium barbarum leaves attenuate obesity through modulating glycolipid levels, oxidative stress, and gut bacterial composition in high-fat diet-fed mice

Traditional herbal therapy made from Lycium barbarum leaves has been said to be effective in treating metabolic diseases, while its exact processes are yet unknown. Natural flavonoids are considered as a secure and reliable method for treating obesity. We thus made an effort to investigate the processes by which flavonoids from L. barbarum leaves (LBLF) reduce obesity. To assess the effectiveness of the intervention following intragastric injection of various dosages of LBLF (50, 100, and 200 mg/kg⋅bw), obese model mice developed via a high-fat diet were utilized. Treatment for LBLF may decrease body weight gain, Lee’s index, serum lipids levels, oxidative stress levels, and hepatic lipids levels. It may also enhance fecal lipids excretion and improve glucose tolerance. Additionally, LBLF therapy significantly restored gut dysfunction brought on by a high-fat diet by boosting gut bacterial diversities and altering the composition of the gut bacterial community by elevating probiotics and reducing harmful bacteria

MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving constantly and yield new adversarial examples to bypass the existing detectors. It needs to collect tens of thousands samples to train detectors, while the new attacks evolve much more frequently than the high-cost data collection. Thus, this situation leads the newly evolved attack samples to remain in small scales. To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples. Specifically, the learning consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack. To validate the effectiveness of our approach, we construct the benchmarks with few-shot-fashion protocols based on three conventional datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are conducted on them to verify the superiority of our approach with respect to the traditional adversarial attack detection methods.Comment: 10 pages, 2 figures, accepted as the conference paper of Proceedings of the 27th ACM International Conference on Multimedia (MM'19

Emergence of V1 connectivity pattern and Hebbian rule in a performance-optimized artificial neural network

AbstractThe connectivity pattern and function of the recurrent connections in the primary visual cortex (V1) have been studied for a long time. But the underlying mechanism remains elusive. We hypothesize that the recurrent connectivity is a result of performance optimization in recognizing images. To test this idea, we added recurrent connections within the first convolutional layer in a standard convolutional neural network, mimicking the recurrent connections in the V1, then trained the network for image classification using the back-propagation algorithm. We found that the trained connectivity pattern was similar to those discovered in biological experiments. According to their connectivity, the neurons were categorized into simple and complex neurons. The recurrent synaptic weight between two simple neurons is determined by the inner product of their receptive fields, which is consistent with the Hebbian rule. Functionally, the recurrent connections linearly amplify the feedforward inputs to simple neurons and determine the properties of complex neurons. The agreement between the model results and biological findings suggests that it is possible to use deep learning to further our understanding of the connectome.</jats:p