Composite Adversarial Attacks

Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model security. In this paper, a new procedure called Composite Adversarial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms and their hyper-parameters from a candidate pool of \textbf{32 base attackers}. We design a search space where attack policy is represented as an attacking sequence, i.e., the output of the previous attacker is used as the initialization input for successors. Multi-objective NSGA-II genetic algorithm is adopted for finding the strongest attack policy with minimum complexity. The experimental result shows CAA beats 10 top attackers on 11 diverse defenses with less elapsed time (\textbf{6 $\times$ faster than AutoAttack}), and achieves the new state-of-the-art on $l_{\infty}$, $l_{2}$ and unrestricted adversarial attacks.Comment: To appear in AAAI 2021, code will be released late

COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts

Practical object detection application can lose its effectiveness on image inputs with natural distribution shifts. This problem leads the research community to pay more attention on the robustness of detectors under Out-Of-Distribution (OOD) inputs. Existing works construct datasets to benchmark the detector's OOD robustness for a specific application scenario, e.g., Autonomous Driving. However, these datasets lack universality and are hard to benchmark general detectors built on common tasks such as COCO. To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts. COCO-O has a large distribution gap with training data and results in a significant 55.7% relative performance drop on a Faster R-CNN detector. We leverage COCO-O to conduct experiments on more than 100 modern object detectors to investigate if their improvements are credible or just over-fitting to the COCO test set. Unfortunately, most classic detectors in early years do not exhibit strong OOD generalization. We further study the robustness effect on recent breakthroughs of detector's architecture design, augmentation and pre-training techniques. Some empirical findings are revealed: 1) Compared with detection head or neck, backbone is the most important part for robustness; 2) An end-to-end detection transformer design brings no enhancement, and may even reduce robustness; 3) Large-scale foundation models have made a great leap on robust object detection. We hope our COCO-O could provide a rich testbed for robustness study of object detection. The dataset will be available at \url{https://github.com/alibaba/easyrobust/tree/main/benchmarks/coco_o}.Comment: To appear in ICCV2023, https://github.com/alibaba/easyrobust/tree/main/benchmarks/coco_

Effect of visit-to-visit blood pressure variability on mild cognitive impairment and probable dementia in hypertensive patients receiving standard and intensive blood pressure treatment

BackgroundHigh visit-to-visit blood pressure variability (BPV) and hypertension are risk factors for mild cognitive impairment (MCI) and probable dementia (PD). Few articles assessed the effect of BPV on the MCI and PD in intensive blood pressure treatment and the different functions of three types of visit-to-visit BPV: systolic blood pressure variability (SBPV), diastolic blood pressure variability (DBPV) and pulse pressure variability (PPV).MethodsWe performed a post hoc analysis of the SPRINT MIND trial. The primary outcomes were MCI and PD. BPV was measured by average real variability (ARV). The Kaplan-Meier curves were used to clarify the difference in tertiles of BPV. We fit Cox proportional hazards models to our outcome. We also did an interaction analysis between the intensive and standard groups.ResultsWe enrolled 8,346 patients in the SPRINT MIND trial. The incidence of MCI and PD in the intensive group was lower than that in the standard group. 353 patients had MCI and 101 patients had PD in the standard group while 285 patients had MCI and 75 patients had PD in the intensive group. Tertiles with higher SBPV, DBPV and PPV in the standard group had a higher risk of MCI and PD (all p < 0.05). Meanwhile, higher SBPV and PPV in the intensive group were associated with an increased risk of PD (SBPV: HR(95%) = 2.1 (1.1–3.9), p = 0.026; PPV: HR(95%) = 2.0 (1.1–3.8), p = 0.025 in model 3) and higher SBPV in the intensive group was associated with an increased risk of MCI(HR(95%) = 1.4 (1.2–1.8), p < 0.001 in model 3). The difference between intensive and standard blood pressure treatment was not statistically significant when we considered the effect of the higher BPV on the risk of MCI and PD (all p for interaction >0.05).ConclusionIn this post hoc analysis of the SPRINT MIND trial, we found that higher SBPV and PPV were associated with an increased risk of PD in the intensive group, and higher SBPV was associated with an increased risk of MCI in the intensive group. The effect of higher BPV on the risk of MCI and PD was not significantly different in intensive and standard blood pressure treatment. These findings emphasized the need for clinical work to monitor BPV in intensive blood pressure treatment

HDL Subclass Proteomic Analysis and Functional Implication of Protein Dynamic Change During HDL Maturation

Recent clinical trials reported that increasing high-density lipoprotein-cholesterol (HDL-C) levels does not improve cardiovascular outcomes. We hypothesize that HDL proteome dynamics determine HDL cardioprotective functions. In this study, we characterized proteome profiles in HDL subclasses and established their functional connection. Mouse plasma was fractionized by fast protein liquid chromatography, examined for protein, cholesterial, phospholipid and trigliceride content. Small, medium and large (S/M/L)-HDL subclasseses were collected for proteomic analysis by mass spectrometry. Fifty-one HDL proteins (39 in S-HDL, 27 in M-HDL and 29 in L-HDL) were identified and grouped into 4 functional categories (lipid metabolism, immune response, coagulation, and others). Eleven HDL common proteins were identified in all HDL subclasses. Sixteen, 3 and 7 proteins were found only in S-HDL, M-HDL and L-HDL, respectively. We established HDL protein dynamic distribution in S/M/L-HDL and developed a model of protein composition change during HDL maturation. We found that cholesterol efflux and immune response are essential functions for all HDL particles, and amino acid metabolism is a special function of S-HDL, whereas anti-coagulation is special for M-HDL. Pon1 is recruited into M/L-HDL to provide its antioxidative function. ApoE is incorporated into L-HDL to optimize its cholesterial clearance function. Next, we acquired HDL proteome data from Pubmed and identified 12 replicated proteins in human and mouse HDL particle. Finally, we extracted 3 shared top moleccular pathways (LXR/RXR, FXR/RXR and acute phase response) for all HDL particles and 5 top disease/bio-functions differentially related to S/M/L-HDL subclasses, and presented one top net works for each HDL subclass. We conclude that beside their essencial functions of cholesterol efflux and immune response, HDL aquired antioxidative and cholesterol clearance functions by recruiting Pon1 and ApoE during HDL maturation

SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec , a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode

Fe/MOF based platform for NIR laser induced efficient PDT/PTT of cancer

Introduction: Photodynamic therapy (PDT) and photothermal therapy (PTT) are widely used in the treatment of tumors. However, their application in the treatment of clinical tumors is limited by the complexity and irreversible hypoxia environment generated by tumor tissues. To overcome this limitation, a nanoparticle composed of indocyanine green (ICG) and Fe-MOF-5 was developed.Methods: We prepared F-I@FM5 and measured its morphology, particle size, and stability. Its enzyme like ability and optical effect was verified. Then we used MTT, staining and flow cytometry to evaluated the anti-tumor effect on EMT-6 cells in vitro. Finally, the anti-tumor effect in vivo has been studied on EMT-6 tumor bearing mice.Results: For the composite nanoparticle, we confirmed that Fe-MOF-5 has the best nanozyme activity. In addition, it has excellent photothermal conversion efficiency and generates reactive oxygen species (ROS) under near-infrared light irradiation (808 nm). The composite nanoparticle showed good tumor inhibition effect in vitro and in vivo, which was superior to the free ICG or Fe-MOF-5 alone. Besides, there was no obvious cytotoxicity in major organs within the effective therapeutic concentration.Discussion: Fe-MOF-5 has the function of simulating catalase, which can promote the decomposition of excessive H2O2 in the tumor microenvironment and produce oxygen to improve the hypoxic environment. The improvement of tumor hypoxia can enhance the efficacy of PDT and PTT. This research not only provides an efficient and stable anti-tumor nano platform, but also has broad application prospects in the field of tumor therapy, and provides a new idea for the application of MOF as an important carrier material in the field of photodynamic therapy