Boosting Adversarial Attacks by Leveraging Decision Boundary Information

Due to the gap between a substitute model and a victim model, the gradient-based noise generated from a substitute model may have low transferability for a victim model since their gradients are different. Inspired by the fact that the decision boundaries of different models do not differ much, we conduct experiments and discover that the gradients of different models are more similar on the decision boundary than in the original position. Moreover, since the decision boundary in the vicinity of an input image is flat along most directions, we conjecture that the boundary gradients can help find an effective direction to cross the decision boundary of the victim models. Based on it, we propose a Boundary Fitting Attack to improve transferability. Specifically, we introduce a method to obtain a set of boundary points and leverage the gradient information of these points to update the adversarial examples. Notably, our method can be combined with existing gradient-based methods. Extensive experiments prove the effectiveness of our method, i.e., improving the success rate by 5.6% against normally trained CNNs and 14.9% against defense CNNs on average compared to state-of-the-art transfer-based attacks. Further we compare transformers with CNNs, the results indicate that transformers are more robust than CNNs. However, our method still outperforms existing methods when attacking transformers. Specifically, when using CNNs as substitute models, our method obtains an average attack success rate of 58.2%, which is 10.8% higher than other state-of-the-art transfer-based attacks

Frequency Domain Model Augmentation for Adversarial Attack

For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that the transferability of adversarial examples can be improved by attacking diverse models simultaneously, model augmentation methods which simulate different models by using transformed images are proposed. However, existing transformations for spatial domain do not translate to significantly diverse augmented models. To tackle this issue, we propose a novel spectrum simulation attack to craft more transferable adversarial examples against both normally trained and defense models. Specifically, we apply a spectrum transformation to the input and thus perform the model augmentation in the frequency domain. We theoretically prove that the transformation derived from frequency domain leads to a diverse spectrum saliency map, an indicator we proposed to reflect the diversity of substitute models. Notably, our method can be generally combined with existing attacks. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our method, \textit{e.g.}, attacking nine state-of-the-art defense models with an average success rate of \textbf{95.4\%}. Our code is available in \url{https://github.com/yuyang-long/SSA}.Comment: Accepted by ECCV 202

LKCA: Large Kernel Convolutional Attention

We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it with a single large kernel convolution. LKCA combines the advantages of convolutional neural networks and visual transformers, possessing a large receptive field, locality, and parameter sharing. We explained the superiority of LKCA from both convolution and attention perspectives, providing equivalent code implementations for each view. Experiments confirm that LKCA implemented from both the convolutional and attention perspectives exhibit equivalent performance. We extensively experimented with the LKCA variant of ViT in both classification and segmentation tasks. The experiments demonstrated that LKCA exhibits competitive performance in visual tasks. Our code will be made publicly available at https://github.com/CatworldLee/LKCA

Molecular epidemiology of dengue viruses in southern China from 1978 to 2006

To investigate molecular epidemiology of dengue viruses (DENV) in southern China, a total of 14 dengue isolates were collected in southern China during each epidemic year between 1978 and 2006 and their full-length genome sequences were obtained by using RT-PCR method. The E gene sequences from additional 6 dengue fever patients in Guangzhou in 2006 were also obtained by using RT-PCR method. Combined with DENVs sequences published in GenBank, phylogenetic analysis and recombination analysis were performed. One hundred and twenty-five E gene sequences and 60 complete genome sequences published in the GenBank were also involved. Phylogenetic analysis showed that there was a wide genetic diversity of DENVs isolated in southern China. DENV-1 strains exist in almost all of the clades of genotype I and IV except the Asia 1 clade of genotype I; DENV-2 stains are grouped into four of the five genotypes except American genotype. DENV-4 strains are grouped into 2 genotypes (I and II). Phylogenetic analysis also showed that all DENV-4 isolates and two DENV-2 isolates were closely related to the prior isolates from neighboring Southeast Asia countries. The DENV-1 strain isolated during the 2006 epidemic is highly homologous to the strains isolated during the 2001 epidemic

MELK mediates the stability of EZH2 through site-specific phosphorylation in extranodal natural killer/ T-cell lymphoma

Oncogenic EZH2 is overexpressed and extensively involved in the pathophysiology of different cancers including extranodal natural killer/T-cell lymphoma (NKTL). However, the mechanisms regarding EZH2 upregulation is poorly understood, and it still remains untargetable in NKTL. In this study, we examine EZH2 protein turnover in NKTL and identify MELK kinase as a regulator of EZH2 ubiquitination and turnover. Using quantitative mass spectrometry analysis, we observed a MELK-mediated increase of EZH2 S220 phosphorylation along with a concomitant loss of EZH2 K222 ubiquitination, suggesting a phosphorylation-dependent regulation of EZH2 ubiquitination. MELK inhibition through both chemical and genetic means led to ubiquitination and destabilization of EZH2 protein. Importantly, we determine that MELK is upregulated in NKTL, and its expression correlates with EZH2 protein expression as determined by tissue microarray derived from NKTL patients. FOXM1, which connected MELK to EZH2 signaling in glioma, was not involved in mediating EZH2 ubiquitination. Furthermore, we identify USP36 as the deubiquitinating enzyme that deubiquitinates EZH2 at K222. These findings uncover an important role of MELK and USP36 in mediating EZH2 stability in NKTL. Moreover, MELK overexpression led to decreased sensitivity to bortezomib treatment in NKTL based on deprivation of EZH2 ubiquitination. Therefore, modulation of EZH2 ubiquitination status by targeting MELK may be a new therapeutic strategy for NKTL patients with poor bortezomib response

Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition)

Background: Primary liver cancer, around 75%–85% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China in June 2017, which were updated by the National Health Commission in December 2019, additional high-quality evidence has emerged from researchers worldwide regarding the diagnosis, staging, and treatment of liver cancer, that requires the guidelines to be updated again. The new edition (2022 Edition) was written by more than 100 experts in the field of liver cancer in China, which not only reflects the real-world situation in China, but also may re-shape the nationwide diagnosis and treatment of liver cancer. Key Messages: The new guideline aims to encourage the implementation of evidence-based practice, and improve the national average five-year survival rate for patients with liver cancer, as proposed in the "Health China 2030 Blueprint.