OpenRaiser/Intern-Atlas huggingface dataset:
A blind Robust Image Watermarking Approach exploiting the DFT Magnitude
Venue: arXiv cs.CR 2019
Year: 2019

Due to the current progress in Internet, digital contents (video, audio and images) are widely used. Distribution of multimedia contents is now faster and it allows for easy unauthorized reproduction of information. Digital watermarking came up while trying to solve this problem. Its main idea is to embed a watermark into a host digital content without affecting its quality. Moreover, watermarking can be used in several applications such as authentication, copy control, indexation, Copyright protection, etc. In this paper, we propose a blind robust image watermarking approach as a solution to the problem of copyright protection of digital images. The underlying concept of our method is to apply a discrete cosine transform (DCT) to the magnitude resulting from a discrete Fourier transform (DFT) applied to the original image. Then, the watermark is embedded by modifying the coefficients of the DCT using a secret key to increase security. Experimental results show the robustness of the proposed technique to a wide range of common attacks, e.g., Low-Pass Gaussian Filtering, JPEG compression, Gaussian noise, salt & pepper noise, Gaussian Smoothing and Histogram equalization. The proposed method achieves a Peak signal-to-noise-ration (PSNR) value greater than 66 (dB) and ensures a perfect watermark extraction.
Promoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed
Venue: arXiv cs.CL 2019
Year: 2019

The utility of linguistic annotation in neural machine translation seemed to had been established in past papers. The experiments were however limited to recurrent sequence-to-sequence architectures and relatively small data settings. We focus on the state-of-the-art Transformer model and use comparably larger corpora. Specifically, we try to promote the knowledge of source-side syntax using multi-task learning either through simple data manipulation techniques or through a dedicated model component. In particular, we train one of Transformer attention heads to produce source-side dependency tree. Overall, our results cast some doubt on the utility of multi-task setups with linguistic information. The data manipulation techniques, recommended in previous works, prove ineffective in large data settings. The treatment of self-attention as dependencies seems much more promising: it helps in translation and reveals that Transformer model can very easily grasp the syntactic structure. An important but curious result is, however, that identical gains are obtained by using trivial "linear trees" instead of true dependencies. The reason for the gain thus may not be coming from the added linguistic knowledge but from some simpler regularizing effect we induced on self-attention matrices.
Neurlux: Dynamic Malware Analysis Without Feature Engineering
Venue: arXiv cs.CR 2019
Year: 2019

Malware detection plays a vital role in computer security. Modern machine learning approaches have been centered around domain knowledge for extracting malicious features. However, many potential features can be used, and it is time consuming and difficult to manually identify the best features, especially given the diverse nature of malware. In this paper, we propose Neurlux, a neural network for malware detection. Neurlux does not rely on any feature engineering, rather it learns automatically from dynamic analysis reports that detail behavioral information. Our model borrows ideas from the field of document classification, using word sequences present in the reports to predict if a report is from a malicious binary or not. We investigate the learned features of our model and show which components of the reports it tends to give the highest importance. Then, we evaluate our approach on two different datasets and report formats, showing that Neurlux improves on the state of the art and can effectively learn from the dynamic analysis reports. Furthermore, we show that our approach is portable to other malware analysis environments and generalizes to different datasets.
Toward an Automatic System for Computer-Aided Assessment in Facial Palsy
Venue: arXiv cs.CV 2019
Year: 2019

Importance: Machine learning (ML) approaches to facial landmark localization carry great clinical potential for quantitative assessment of facial function as they enable high-throughput automated quantification of relevant facial metrics from photographs. However, translation from research settings to clinical applications requires important improvements. Objective: To develop an ML algorithm for accurate facial landmarks localization in photographs of facial palsy patients, and use it as part of an automated computer-aided diagnosis system. Design, Setting, and Participants: Facial landmarks were manually localized in portrait photographs of eight expressions obtained from 200 facial palsy patients and 10 controls. A novel ML model for automated facial landmark localization was trained using this disease-specific database. Model output was compared to manual annotations and the output of a model trained using a larger database consisting only of healthy subjects. Model accuracy was evaluated by the normalized root mean square error (NRMSE) between algorithms' prediction and manual annotations. Results: Publicly available algorithms provide poor results when applied to patients compared to healthy controls (NRMSE, 8.56 +/- 2.16 vs. 7.09 +/- 2.34, p << 0.01). We found significant improvement in facial landmark localization accuracy for the clinical population when using a model trained with a relatively small number patients' photographs (1440) compared to a model trained using several thousand more images of healthy faces (NRMSE, 6.03 +/- 2.43 vs. 8.56 +/- 2.16, p << 0.01). Conclusions: Retraining a landmark detection model with a small number of clinical images significantly improved landmark detection performance in frontal view photographs of the clinical population. These results represent the first steps towards an automatic system for computer-aided assessment in facial palsy.
Study of Deep Generative Models for Inorganic Chemical Compositions
Venue: arXiv cs.LG 2019
Year: 2019

Generative models based on generative adversarial networks (GANs) and variational autoencoders (VAEs) have been widely studied in the fields of image generation, speech generation, and drug discovery, but, only a few studies have focused on the generation of inorganic materials. Such studies use the crystal structures of materials, but material researchers rarely store this information. Thus, we generate chemical compositions without using crystal information. We use a conditional VAE (CondVAE) and a conditional GAN (CondGAN) and show that CondGAN using the bag-of-atom representation with physical descriptors generates better compositions than other generative models. Also, we evaluate the effectiveness of the Metropolis-Hastings-based atomic valency modification and the extrapolation performance, which is important to material discovery.
Team PFDet's Methods for Open Images Challenge 2019
Venue: arXiv cs.CV 2019
Year: 2019

We present the instance segmentation and the object detection method used by team PFDet for Open Images Challenge 2019. We tackle a massive dataset size, huge class imbalance and federated annotations. Using this method, the team PFDet achieved 3rd and 4th place in the instance segmentation and the object detection track, respectively.
Strong Log-Concavity Does Not Imply Log-Submodularity
Venue: arXiv cs.LG 2019
Year: 2019

We disprove a recent conjecture regarding discrete distributions and their generating polynomials stating that strong log-concavity implies log-submodularity.
Electromagnetic fault injection against a System-on-Chip, toward new micro-architectural fault models
Venue: arXiv cs.CR 2019
Year: 2019

Electromagnetic fault injection (EMFI) is a well known technique used to disturb the behaviour of a chip for weakening its security. These attacks are mostly done on simple microcontrollers. On these targets, the fault effects are relatively simple and understood. Exploiting EMFI on modern system-on-chips (SoCs), the fast and complex chips ubiquitous today, requires to understand the impact of such faults. In this paper, we propose an experimental setup and a forensic process to create exploitable faults and assess their impact on the SoC micro-architecture. On our targeted SoC (a BCM2837), the observed behaviours are radically different to what were obtained with state-of-the-art fault injection attacks on microcontrollers. SoC subsystems (L1 caches, L2 cache, memory management unit (MMU)) can be individually targeted leading to new fault models. We also highlight the differences in the fault impact with and without an operating system (OS). This shows the importance of the software layers in the exploitation of a fault. With this work, we demonstrate that the complexity and the speed of SoCs do not protect them against hardware fault attacks. To conclude our work, we introduce countermeasures to protect the SoC caches and MMU against EMFI attacks based on the disclosed faults effects.
Exploring Lexicon-Free Modeling Units for End-to-End Korean and Korean-English Code-Switching Speech Recognition
Venue: arXiv cs.SD 2019
Year: 2019

As the character-based end-to-end automatic speech recognition (ASR) models evolve, the choice of acoustic modeling units becomes important. Since Korean is a fairly phonetic language and has a unique writing system with its own Korean alphabet, it's worth investigating modeling units for an end-to-end Korean ASR task. In this work, we introduce lexicon-free modeling units in Korean, and explore them using a hybrid CTC/Attention-based encoder-decoder model. Five lexicon-free units are investigated: Syllable-based Korean character (with English character for a code-switching task), Korean Jamo character (with English character), sub-word on syllable-based character (with sub-word in English), sub-word on Jamo character (with sub-words in English), and finally byte unit, which is a universal one across language. Experiments on Zeroth-Korean (51.6 hrs) and Medical Record (2530 hrs) are done for Korean and Korean-English code-switching ASR tasks, respectively. Sequence-to-sequence learning with sub-words based on Korean syllables (and sub-words in English) performs the best for both tasks without lexicon and an extra language model integration.
MediaEval 2019: Concealed FGSM Perturbations for Privacy Preservation
Venue: arXiv cs.LG 2019
Year: 2019

This work tackles the Pixel Privacy task put forth by MediaEval 2019. Our goal is to manipulate images in a way that conceals them from automatic scene classifiers while preserving the original image quality. We use the fast gradient sign method, which normally has a corrupting influence on image appeal, and devise two methods to minimize the damage. The first approach uses a map of pixel locations that are either salient or flat, and directs perturbations away from them. The second approach subtracts the gradient of an aesthetics evaluation model from the gradient of the attack model to guide the perturbations towards a direction that preserves appeal. We make our code available at: https://git.io/JesXr.
AeroVR: Virtual Reality-based Teleoperation with Tactile Feedback for Aerial Manipulation
Venue: arXiv cs.RO 2019
Year: 2019

Drone application for aerial manipulation is tested in such areas as industrial maintenance, supporting the rescuers in emergencies, and e-commerce. Most of such applications require teleoperation. The operator receives visual feedback from the camera installed on a robot arm or drone. As aerial manipulation requires delicate and precise motion of robot arm, the camera data delay, narrow field of view, and blurred images caused by drone dynamics can lead the UAV to crash. The paper focuses on the development of a novel teleoperation system for aerial manipulation using Virtual Reality (VR). The controlled system consists of UAV with a 4-DoF robotic arm and embedded sensors. VR application presents the digital twin of drone and remote environment to the user through a head-mounted display (HMD). The operator controls the position of the robotic arm and gripper with VR trackers worn on the arm and tracking glove with vibrotactile feedback. Control data is translated directly from VR to the real robot in real-time. The experimental results showed a stable and robust teleoperation mediated by the VR scene. The proposed system can considerably improve the quality of aerial manipulations.
Security analysis of an audio data encryption scheme based on key chaining and DNA encoding
Venue: arXiv cs.CR 2019
Year: 2019

Fairly recently, a new encryption scheme for audio data encryption has been proposed by Naskar, P.K., et al. The cryptosystem is based on substitution-permutation encryption structure using DNA encoding at the substitution stage, in which the key generation is based on a key chaining algorithm that generates new key block for every plain block using a logistic chaotic map. After some several statistical tests done by the authors of the scheme, they claimed that their cryptosystem is robust and can resist conventional cryptanalysis attacks. Negatively, in this paper we show the opposite: the scheme is extremely weak against chosen ciphertext and plaintext attacks thus only two chosen plaintexts of 32 byte size are sufficient to recover the equivalent key used for encryption. The cryptosystem's shuffling process design is vulnerable which allow us recovering the unknown original plaintext by applying repeated encryptions. Our study proves that the scheme is extremely weak and should not be used for any information security or cryptographic concern. Lessons learned from this cryptanalytic paper are then outlined in order to be considered in further designs and proposals.
A Note on Our Submission to Track 4 of iDASH 2019
Venue: arXiv cs.CR 2019
Year: 2019

iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology. In 2019, one track asked for multi-party computation implementations of training of a machine learning model suitable for two datasets from cancer research. In this note, we describe our solution submitted to the competition. We found that the training can be run on three AWS c5.9xlarge instances in less then one minute using MPC tolerating one semi-honest corruption, and less than ten seconds at a slightly lower accuracy.
Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf Databases
Venue: arXiv cs.DB 2019
Year: 2019

Commercial off-the-shelf DataBase Management Systems (DBMSes) are highly optimized to process a wide range of queries by means of carefully designed indexing and query planning. However, many aggregate range queries are usually performed by DBMSes using sequential scans, and certain needs, like storing Authenticated Data Structures (ADS), are not supported at all. Theoretically, these needs could be efficiently fulfilled adopting specific kinds of indexing, which however are normally ruled-out in DBMSes design. We introduce the concept of overlay index: an index that is meant to be stored in a standard database, alongside regular data and managed by regular software, to complement DBMS capabilities. We show a data structure, that we call DB-tree, that realizes an overlay index to support a wide range of custom aggregate range queries as well as ADSes, efficiently. All DB-trees operations can be performed by executing a small number of queries to the DBMS, that can be issued in parallel in one or two query rounds, and involves a logarithmic amount of data. We experimentally evaluate the efficiency of DB-trees showing that our approach is effective, especially if data updates are limited.
Model-Driven Process Enactment for NFV Systems with MAPLE
Venue: arXiv cs.SE 2019
Year: 2019

The Network Functions Virtualization (NFV) advent is making way for the rapid deployment of network services (NS) for telecoms. Automation of network service management is one of the main challenges currently faced by the NFV community. Explicitly defining a process for the design, deployment, and management of network services and automating it is therefore highly desirable and beneficial for NFV systems. The use of model-driven orchestration means has been advocated in this context. As part of this effort to support automated process execution, we propose a process enactment approach with NFV systems as the target application domain. Our process enactment approach is megamodel-based. An integrated process modelling and enactment environment, MAPLE, has been built into Papyrus for this purpose. Process modelling is carried out with UML activity diagrams. The enactment environment transforms the process model to a model transformation chain, and then orchestrates it with the use of megamodels. In this paper we present our approach and environment MAPLE, its recent extension with new features as well as application to an enriched case study consisting of NS design and onboarding process.
Learning to Track Any Object
Venue: arXiv cs.CV 2019
Year: 2019

Object tracking can be formulated as "finding the right object in a video". We observe that recent approaches for class-agnostic tracking tend to focus on the "finding" part, but largely overlook the "object" part of the task, essentially doing a template matching over a frame in a sliding-window. In contrast, class-specific trackers heavily rely on object priors in the form of category-specific object detectors. In this work, we re-purpose category-specific appearance models into a generic objectness prior. Our approach converts a category-specific object detector into a category-agnostic, object-specific detector (i.e. a tracker) efficiently, on the fly. Moreover, at test time the same network can be applied to detection and tracking, resulting in a unified approach for the two tasks. We achieve state-of-the-art results on two recent large-scale tracking benchmarks (OxUvA and GOT, using external data). By simply adding a mask prediction branch, our approach is able to produce instance segmentation masks for the tracked object. Despite only using box-level information on the first frame, our method outputs high-quality masks, as evaluated on the DAVIS '17 video object segmentation benchmark.
Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity
Venue: arXiv cs.CL 2019
Year: 2019

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties with data collection. These predominantly yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. We (ii) conduct an extensive set of experiments that indicate a general lack of cross-domain generalization of classifiers trained on these sources, and openly provide this framework to replicate and extend our evaluation criteria. Finally, we (iii) present an effective crowdsourcing method: simulating real-life bullying scenarios in a lab setting generates plausible data that can be effectively used to enrich real data. This largely circumvents the restrictions on data that can be collected, and increases classifier performance. We believe these contributions can aid in improving the empirical practices of future research in the field.
Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency
Venue: arXiv cs.LG 2019
Year: 2019

Current multi-reference style transfer models for Text-to-Speech (TTS) perform sub-optimally on disjoints datasets, where one dataset contains only a single style class for one of the style dimensions. These models generally fail to produce style transfer for the dimension that is underrepresented in the dataset. In this paper, we propose an adversarial cycle consistency training scheme with paired and unpaired triplets to ensure the use of information from all style dimensions. During training, we incorporate unpaired triplets with randomly selected reference audio samples and encourage the synthesized speech to preserve the appropriate styles using adversarial cycle consistency. We use this method to transfer emotion from a dataset containing four emotions to a dataset with only a single emotion. This results in a 78% improvement in style transfer (based on emotion classification) with minimal reduction in fidelity and naturalness. In subjective evaluations our method was consistently rated as closer to the reference style than the baseline. Synthesized speech samples are available at: https://sites.google.com/view/adv-cycle-consistent-tts
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Venue: arXiv cs.LG 2019
Year: 2019

Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized into supervised learning methods and unsupervised or model-based learning methods. Supervised learning methods have recently shown success in image restoration tasks. However, they often rely on large training sets. Model-based learning methods such as dictionary or transform learning do not require large or paired training sets and often have good generalization properties, since they learn general properties of CT image sets. Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms. In this paper, we propose a new Supervised-UnsuPERvised (SUPER) reconstruction framework for LDCT image reconstruction that combines the benefits of supervised learning methods and (unsupervised) transform learning-based methods such as PWLS-ULTRA that involve highly image-adaptive clustering. The SUPER model consists of several layers, each of which includes a deep network learned in a supervised manner and an unsupervised iterative method that involves image-adaptive components. The SUPER reconstruction algorithms are learned in a greedy manner from training data. The proposed SUPER learning methods dramatically outperform both the constituent supervised learning-based networks and iterative algorithms for LDCT, and use much fewer iterations in the iterative reconstruction modules.
Analysis on Advancement of Hybrid Fuzzy Sliding Mode Controllers for Nonlinear Systems
Venue: arXiv cs.RO 2019
Year: 2019

Chattering phenomena is the major problem affecting sliding mode control (SMC). Also, finding a suitable structure and appropriate parameters values of fuzzy logic system (FLS) is a complex and difficult task. In addition, the stability of a general FLS is difficult to guarantee. Many types of combinations between FLS and SMC have been used to form an intelligent and robust controller that deviates from the limitations of each constituent and benefit from the advantages of each constituent. In this study, a survey of recent developments on the Hybridization of FLS (type-1) and SMC is presented. In addition, the differences between using the SMC in FLC or using FLC in SMC as well as their limitation and advantages are highlighted. It is found that the majority of the combinations made are intended to approximate the nonlinear sliding surface within the boundary layer. Limitations of the previous approaches and future research directions are pointed out. For novice researchers, this survey can serve as a foundation for their work while for expert researchers this review can serve as a benchmark for further advancement and exploration of other hybridization methods.