Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments also confirm excellent robustness under noise and insensitivity to parameter variations.

Highly accurate optic flow computation with theoretically justified warping

Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in the use of mobile devices, users must constantly worry about security and privacy, as the loss of a mobile device could compromise personal information. To deal with this problem, continuous authentication systems (also known as active authentication systems) have been proposed, in which users are continuously monitored after initial access to the mobile device. In this article, we provide an overview of different continuous authentication methods on mobile devices. We discuss the merits and drawbacks of the available approaches and identify promising avenues of research in this rapidly evolving field.

Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges

Recent methods and databases in vision-based hand gesture recognition

We present a novel deep-learning-based approach for one-class transfer learning in which labeled data from an unrelated task is used for feature learning in one-class classification. The proposed method operates on top of a convolutional neural network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss, are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection, and mobile active authentication datasets show that the proposed deep one-class (DOC) classification method achieves significant improvements over the state-of-the-art.

Learning Deep Features for One-Class Classification

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors — front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.

Active user authentication for smartphones: A challenge data set and benchmark results

In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods. The first method generates universal perturbations for target classes and the second generates image specific perturbations. Extensive experiments are conducted on MNIST and CIFAR10 datasets to provide insights about the proposed algorithms and show their effectiveness.

UPSET and ANGRI : Breaking High Performance Image Classifiers.

In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments in the frame and cluster the results to obtain the region which is most likely to contain a face. Extensive experimentation on a mobile dataset of 50 users shows that our method performs better than many state-of-the-art face detection methods in terms of accuracy and processing speed.

Partial face detection for continuous authentication

A template matching approach of one-shot-learning gesture recognition

Power Quality means to maintain purely sinusoidal current wave form in phase with a purely sinusoidal voltage wave form. Power quality improvement using traditional compensation methods include many disadvantages like electromagnetic interference, possible resonance, fixed compensation, bulkiness etc. So power system and power electronic engineers need to develop adjustable and dynamic solutions using custom power devices. These power conditioning equipments use static power electronic converters to improve the power quality of distribution system customers. The devices include Active Power Filter (APF), dynamic voltage restorer (DVR) and Unified Power Quality Conditioner (UPQC). APF is a compensator used to eliminate the disturbances in current. There are basically two types of APFs: the shunt type and the series type. This paper examines the control of Shunt Active Power Filter (SAPF) from two different aspects: Synchronous Detection Method (SDM) and digital control based on instantaneous power theory (p-q theory). Simulation results using MATLAB SIMULINK demonstrates the application of these methods to the control of APF. Moreover, this work shows that digital control provides better power quality improvement than SDM.

Upal Mahbub

Papers

Active user authentication for smartphones: A challenge data set and benchmark results

UPSET and ANGRI : Breaking High Performance Image Classifiers.

Partial face detection for continuous authentication

A template matching approach of one-shot-learning gesture recognition

Synchronous detection and digital control of Shunt Active Power Filter in power quality improvement