110 research outputs found
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
An off-the-shelf model as a commercial service could be stolen by model
stealing attacks, posing great threats to the rights of the model owner. Model
fingerprinting aims to verify whether a suspect model is stolen from the victim
model, which gains more and more attention nowadays. Previous methods always
leverage the transferable adversarial examples as the model fingerprint, which
is sensitive to adversarial defense or transfer learning scenarios. To address
this issue, we consider the pairwise relationship between samples instead and
propose a novel yet simple model stealing detection method based on SAmple
Correlation (SAC). Specifically, we present SAC-w that selects wrongly
classified normal samples as model inputs and calculates the mean correlation
among their model outputs. To reduce the training time, we further develop
SAC-m that selects CutMix Augmented samples as model inputs, without the need
for training the surrogate models or generating adversarial examples. Extensive
results validate that SAC successfully defends against various model stealing
attacks, even including adversarial training or transfer learning, and detects
the stolen models with the best performance in terms of AUC across different
datasets and model architectures. The codes are available at
https://github.com/guanjiyang/SAC
Test-Time Backdoor Defense via Detecting and Repairing
Deep neural networks have played a crucial part in many critical domains,
such as autonomous driving, face recognition, and medical diagnosis. However,
deep neural networks are facing security threats from backdoor attacks and can
be manipulated into attacker-decided behaviors by the backdoor attacker. To
defend the backdoor, prior research has focused on using clean data to remove
backdoor attacks before model deployment. In this paper, we investigate the
possibility of defending against backdoor attacks at test time by utilizing
partially poisoned data to remove the backdoor from the model. To address the
problem, a two-stage method Test-Time Backdoor Defense (TTBD) is proposed. In
the first stage, we propose a backdoor sample detection method DDP to identify
poisoned samples from a batch of mixed, partially poisoned samples. Once the
poisoned samples are detected, we employ Shapley estimation to calculate the
contribution of each neuron's significance in the network, locate the poisoned
neurons, and prune them to remove backdoor in the models. Our experiments
demonstrate that TTBD removes the backdoor successfully with only a batch of
partially poisoned data across different model architectures and datasets
against different types of backdoor attacks
Learning View-Model Joint Relevance for 3D Object Retrieval
3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graph-based framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method
SigFlux: A novel network feature to evaluate the importance of proteins in signal transduction networks
BACKGROUND: Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. RESULTS: We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. CONCLUSION: SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts
CPARR: Category-based Proposal Analysis for Referring Relationships
The task of referring relationships is to localize subject and object
entities in an image satisfying a relationship query, which is given in the
form of \texttt{}. This requires simultaneous
localization of the subject and object entities in a specified relationship. We
introduce a simple yet effective proposal-based method for referring
relationships. Different from the existing methods such as SSAS, our method can
generate a high-resolution result while reducing its complexity and ambiguity.
Our method is composed of two modules: a category-based proposal generation
module to select the proposals related to the entities and a predicate analysis
module to score the compatibility of pairs of selected proposals. We show
state-of-the-art performance on the referring relationship task on two public
datasets: Visual Relationship Detection and Visual Genome.Comment: CVPR 2020 Workshop on Multimodal Learnin
A nonparametric model for quality control of database search results in shotgun proteomics
<p>Abstract</p> <p>Background</p> <p>Analysis of complex samples with tandem mass spectrometry (MS/MS) has become routine in proteomic research. However, validation of database search results creates a bottleneck in MS/MS data processing. Recently, methods based on a randomized database have become popular for quality control of database search results. However, a consequent problem is the ignorance of how to combine different database search scores to improve the sensitivity of randomized database methods.</p> <p>Results</p> <p>In this paper, a multivariate nonlinear discriminate function (DF) based on the multivariate nonparametric density estimation technique was used to filter out false-positive database search results with a predictable false positive rate (FPR). Application of this method to control datasets of different instruments (LCQ, LTQ, and LTQ/FT) yielded an estimated FPR close to the actual FPR. As expected, the method was more sensitive when more features were used. Furthermore, the new method was shown to be more sensitive than two commonly used methods on 3 complex sample datasets and 3 control datasets.</p> <p>Conclusion</p> <p>Using the nonparametric model, a more flexible DF can be obtained, resulting in improved sensitivity and good FPR estimation. This nonparametric statistical technique is a powerful tool for tackling the complexity and diversity of datasets in shotgun proteomics.</p
Structural Design, Fabrication and Evaluation of Resorbable Fiber-Based Tissue Engineering Scaffolds
The use of tissue engineering to regenerate viable tissue relies on selecting the appropriate cell line, developing a resorbable scaffold and optimizing the culture conditions including the use of biomolecular cues and sometimes mechanical stimulation. This review of the literature focuses on the required scaffold properties, including the polymer material, the structural design, the total porosity, pore size distribution, mechanical performance, physical integrity in multiphase structures as well as surface morphology, rate of resorption and biocompatibility. The chapter will explain the unique advantages of using textile technologies for tissue engineering scaffold fabrication, and will delineate the differences in design, fabrication and performance of woven, warp and weft knitted, braided, nonwoven and electrospun scaffolds. In addition, it will explain how different types of tissues can be regenerated by each textile technology for a particular clinical application. The use of different synthetic and natural resorbable polymer fibers will be discussed, as well as the need for specialized finishing techniques such as heat setting, cross linking, coating and impregnation, depending on the tissue engineering application
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