arXiv subjects

Min Wu

Publications and source records attributed to Min Wu.

At least 217 records · Page 12Linked to original sources

arXiv2019

StegoAppDB: a Steganography Apps Forensics Image Database

In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retainthe original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAppDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.

eess.IV↗

arXiv2019

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations. We demonstrate that, under the assumption of Lipschitz continuity, both problems can be approximated using finite optimisation by discretising the input space, and the approximation has provable guarantees, i.e., the error is bounded. We then show that the resulting optimisation problems can be reduced to the solution of two-player turn-based games, where the first player selects features and the second perturbs the image within the feature. While the second player aims to minimise the distance to an adversarial example, depending on the optimisation objective the first player can be cooperative or competitive. We employ an anytime approach to solve the games, in the sense of approximating the value of a game by monotonically improving its upper and lower bounds. The Monte Carlo tree search algorithm is applied to compute upper bounds for both games, and the Admissible A* and the Alpha-Beta Pruning algorithms are, respectively, used to compute lower bounds for the maximum safety radius and feature robustness games. When working on the upper bound of the maximum safe radius problem, our tool demonstrates competitive performance against existing adversarial example crafting algorithms. Furthermore, we show how our framework can be deployed to evaluate pointwise robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.

cs.LG↗

arXiv2018

Thermal tuning of the carrier density in Dirac semimetal Cd3As2 nanoplates

The tunable carrier density plays a key role in the investigation of novel transport properties in three-dimensional topological semimetals. Here we demonstrate that the carrier density as well as the mobility of Dirac semimetal Cd3As2 nanoplates can be effectively tuned by the in-situ thermal treatment at 350 K for one hour, both showing a non-monotonic evolution with the thermal cycling treatments. The upward shift of Fermi level relative to the Dirac nodes blurs the surface Fermi-arc states, accompanying with an anomalous phase shift of oscillations of bulk states due to the change of topology of electrons. Meanwhile, the peaks of oscillations of bulk longitudinal magnetoresistivity shift at high fields due to their coupling to the oscillations of the surface Fermi-arc states. Our work provides a thermal control knob for manipulations of the quantum states through the carrier density in Dirac semimetal Cd3As2 at high temperature.

cond-mat.mes-hall↗

arXiv2018

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm

Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adversarial perturbations in a neighbourhood around an input. In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples. Then we define global robustness as an expectation of the maximal safe radius over a test data set. We first show that the problem is NP-hard, and then propose an approximate approach to iteratively compute lower and upper bounds on the network's robustness. The approach is \emph{anytime}, i.e., it returns intermediate bounds and robustness estimates that are gradually, but strictly, improved as the computation proceeds; \emph{tensor-based}, i.e., the computation is conducted over a set of inputs simultaneously, instead of one by one, to enable efficient GPU computation; and has \emph{provable guarantees}, i.e., both the bounds and the robustness estimates can converge to their optimal values. Finally, we demonstrate the utility of the proposed approach in practice to compute tight bounds by applying and adapting the anytime algorithm to a set of challenging problems, including global robustness evaluation, competitive $L_0$ attacks, test case generation for DNNs, and local robustness evaluation on large-scale ImageNet DNNs. We release the code of all case studies via GitHub.

cs.LG↗

arXiv2018

SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL2MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in SL2MF. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of SL2MF.

cs.LG↗

arXiv2018

de Haas-van Alphen study on three-dimensional topological semimetal pyrite PtBi2

We present the systematic de Haas-van Alphen (dHvA) quantum oscillations studies on the recently discovered topological Dirac semimetal pyrite PtBi2 single crystals. Remarkable dHvA oscillations were observed at field as low as 1.5 T. From the analyses of dHvA oscillations, we have extracted high quantum mobility, light effective mass and phase shift factor for Dirac fermions in pyrite PtBi2. From the angular dependence of dHvA oscillations, we have mapped out the topology of the Fermi surface and identified additional oscillation frequencies which were not probed by SdH oscillations.

cond-mat.mtrl-sci↗

arXiv2018

Concolic Testing for Deep Neural Networks

Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for performing concolic testing to increase test coverage. Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.

cs.LG↗

arXiv2018

Tackling Android Stego Apps in the Wild

Digital image forensics is a young but maturing field, encompassing key areas such as camera identification, detection of forged images, and steganalysis. However, large gaps exist between academic results and applications used by practicing forensic analysts. To move academic discoveries closer to real-world implementations, it is important to use data that represent "in the wild" scenarios. For detection of stego images created from steganography apps, images generated from those apps are ideal to use. In this paper, we present our work to perform steg detection on images from mobile apps using two different approaches: "signature" detection, and machine learning methods. A principal challenge of the ML task is to create a great many of stego images from different apps with certain embedding rates. One of our main contributions is a procedure for generating a large image database by using Android emulators and reverse engineering techniques. We develop algorithms and tools for signature detection on stego apps, and provide solutions to issues encountered when creating ML classifiers.

cs.CR↗

arXiv2018

On Assouad dimension and arithmetic progressions in sets defined by digit restrictions

We show that the set defined by digit restrictions contains arbitrarily long arithmetic progressions if and only if its Assouad dimension is one. Moreover, we show that for any $0\le s\le 1$, there exists some set on $\mathbb{R}$ with Hausdorff dimension $s$ whose Fourier dimension is zero and it contains arbitrarily long arithmetic progressions.

math.CA↗

arXiv2018

A nanophotonic all-optical diode for non-reciprocal transmission of circularly polarized lights

All optical diodes (AODs) play an important role in quantum optics and information processing, in which the information is encoded by photons. Only circularly polarized lights are able to carry the spin states of photons, which has been intensively used in quantum computing and information processing and enable new research fields, such as chiral quantum optics. An ideal AOD should be able to work with arbitrary polarizations states, including circularly polarized lights, which has not been demonstrated yet. In this paper, we theoretically demonstrate for the first time a nanophotonic AOD that is able to work with circularly polarized lights. The AOD nanostructure is based on a heterostructure of two-dimension silica and silicon photonic crystals (PhCs). By controlling the effective refractive indices of the PhCs and using an inclined interface, we are able to exploit generalized total reflection principle to achieve non-reciprocal transmission of circularly polarized lights. In addition, the nanophotonic AOD is able to achieve high forward transmittance greater than 0.6 and high contrast ratio close to 1 in a broad wavelength range of 1497 nm to 1666 nm. The designed nanophotonic AOD will find broad applications in optical quantum information processing and computing.

physics.optics↗

arXiv2018

Adaptive Cost-sensitive Online Classification

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, for better trade off between the performance and efficiency, we further introduce the sketching technique into our algorithms, which significantly accelerates the computational speed with quite slight performance loss. Finally, we apply our algorithms to tackle several online anomaly detection tasks from real world. Promising results prove that the proposed algorithms are effective and efficient in solving cost-sensitive online classification problems in various real-world domains.

cs.LG↗

arXiv2018

Quantitative recurrence properties and homogeneous self-similar sets

Let $K$ be a homogeneous self-similar set satisfying the strong separation condition. This paper is concerned with the quantitative recurrence properties of the natural map $T: K\rightarrow K$ induced by the shift. Let $μ$ be the natural self-similar measure supported on $K$. For a positive function $φ$ defined on $\mathbb{N}$, we show that the $μ$-measure of the following set \begin{equation*} R(φ):=\{x\in K: |T^n x-x|<φ(n) \; \text{for infinitely many} \; n\in\mathbb{N}\} \end{equation*} is null or full according to convergence or divergence of a certain series. Moreover, a similar dichotomy law holds for the general Hausdorff measure, which completes the metric theory of this set.

math.DS↗

arXiv2017

Recognition of Fermi-arc states through the magnetoresistance quantum oscillations in Dirac semimetal Cd3As2 nanoplates

The disjointed Fermi-arcs in Weyl semimetals can intertwine with chiral bulk modes and participate in unusual closed magnetic orbits in the presence of a vertical magnetic field. Here we carry out the quantum oscillation study of such unusual Weyl magnetic orbits in Dirac semimetal Cd3As2, a close cousin of Weyl semimetals. We find that extra two-dimensional (2D) quantum oscillations emerge at high field, which superimpose on 3D bulk background and can be attributed to the Weyl magnetic orbits, when the thickness of nanoplates is smaller than the mean free path of the electrons. Further evidence of the 2D quantum oscillations from the Weyl magnetic orbits is provided by the nonlocal detection, which demonstrates an alternative way to study the quantum transport properties of Fermi-arcs under magnetic field.

cond-mat.mes-hall↗

arXiv2017

Probing the Chiral Anomaly by Planar Hall Effect in Three-dimensional Dirac Semimetal Cd3As2 Nanoplates

Searching for exotic transport properties in new topological state of matters is an active topic. One of the most fascinating achievements is the chiral anomaly in recently discovered Weyl semimetals (WSMs), which is manifested as a negative longitudinal magnetoresistance (LMR) in the presence of a magnetic field B parallel to an electric field E. Another predicted key effect closely related to the chiral anomaly is the planar Hall effect (PHE), which has not been identified in WSMs so far. Here we carried out the planar Hall measurements on Cd3As2 nanoplates, and found that, accompanied by the large negative LMR, a PHE with non-zero transverse voltage can be developed while tilting the in-plane magnetic field B away from the electric field E. Further experiments reveal that both the PHE and the negative LMR can be suppressed synchronously by increasing the temperature, but still visible at room temperature, indicating the same origin of these two effects. The observation of PHE in Cd3As2 nanoplates gives another transport evidence for the chiral anomaly and provides a deep insight into the chiral charge pumping in Weyl Fermions system.

cond-mat.mes-hall↗

arXiv2017

Safety Verification of Deep Neural Networks

Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on safety of image classification decisions with respect to image manipulations, such as scratches or changes to camera angle or lighting conditions that would result in the same class being assigned by a human, and define safety for an individual decision in terms of invariance of the classification within a small neighbourhood of the original image. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.

cs.AI↗

arXiv2017

Extremely large magnetoresistance in a topological semimetal candidate pyrite PtBi2

While pyrite-type PtBi2 with face-centered cubic structure has been predicted to be a three-dimensional (3D) Dirac semimetal, experimental study on its physical properties remains absent. Here we report the angular-dependent magnetoresistance (MR) measurements of PtBi2 single-crystal under high magnetic fields. We observed extreme large unsaturated magnetoresistance (XMR) up to 11.2 million percent at T = 1.8 K in a magnetic field of 33 T, which surpasses the previously reported Dirac materials, such as WTe2, LaSb and NbP. The crystals exhibit an ultrahigh mobility and significant Shubnikov-de Hass (SdH) quantum oscillations with nontrivial Berry's phase. Analysis of Hall resistivity indicates that the XMR can be ascribed to the nearly compensated electron and hole. Our experimental results associated with the ab initio calculations suggest that pyrite PtBi2 is a topological semimetal candidate which might provide a platform for exploring topological materials with XMR in noble metal alloys.

cond-mat.mtrl-sci↗

arXiv2017

The exceptional sets on the run-length function of beta-expansions

Let $β> 1$ and the run-length function $r_n(x,β)$ be the maximal length of consecutive zeros amongst the first n digits in the $β$-expansion of $x\in[0,1]$. The exceptional set $$E_{\max}^φ=\left\{x \in [0,1]:\liminf_{n\rightarrow \infty}\frac{r_n(x,β)}{φ(n)}=0, \limsup_{n\rightarrow \infty}\frac{r_n(x,β)}{φ(n)}=+\infty\right\}$$ is investigated, where $φ: \mathbb{N} \rightarrow \mathbb{R}^+$ is a monotonically increasing function with $\lim\limits_{n\rightarrow \infty }φ(n)=+\infty$. We prove that the set $E_{\max}^φ$ is either empty or of full Hausdorff dimension and residual in $[0,1]$ according to the increasing rate of $φ$ .

math.DS↗

arXiv2017

Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling

We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate. We analyze the performance of two classes of estimators, namely the $\ell_1$-regularized maximum likelihood and greedy estimators, for a canonical self-exciting process and characterize the sampling tradeoffs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear and generalized linear models with i.i.d. covariates to those with highly inter-dependent covariates. We further provide simulation studies as well as application to real spiking data from the mouse's lateral geniculate nucleus and the ferret's retinal ganglion cells which agree with our theoretical predictions.

cs.NE↗