Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Evolution of Malware in the Digital Transformation Age

2021 ◽  
pp. 244-266
Author(s):  
Shahid Alam
Keyword(s):  
Leading Edge ◽  
Digital Transformation ◽  
Future Research ◽  
Malware Analysis ◽  
New Wave ◽  
New Techniques ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Native Code

As corporations are stepping into the new digital transformation age and adopting leading-edge technologies such as cloud, mobile, and big data, it becomes crucial for them to contemplate the risks and rewards of this adoption. At the same time, the new wave of malware attacks is posing a severe impediment in implementing these technologies. This chapter discusses some of the complications, challenges, and issues plaguing current malware analysis and detection techniques. Some of the key challenges discussed are automation, native code, obfuscations, morphing, and anti-reverse engineering. Solutions and recommendations are provided to solve some of these challenges. To stimulate further research in this thriving area, the authors highlight some promising future research directions. The authors believe that this chapter provides an auspicious basis for future researchers who intend to know more about the evolution of malware and will act as a motivation for enhancing the current and developing the new techniques for malware analysis and detection.

scholarly journals Transmission and detection techniques for Internet of Bio-Nano Things applications with static and mobile molecular communication: A survey

2021 ◽  
Vol 2 (3) ◽  
pp. 33-78
Author(s):  
Amit K. Shrivastava ◽  
Debanjan Das ◽  
Neeraj Varshney ◽  
Rajarshi Mahapatra
Keyword(s):  
Communication Systems ◽  
Future Research ◽  
Molecular Communication ◽  
Research Directions ◽  
Detection Techniques ◽  
Practical Design ◽  
Future Research Directions ◽  
Experimental Works ◽  
Detection Schemes ◽  
Health Applications

Recent studies have shown that designing communication systems at nanoscale and microscale for the Internet of Bio-Nano Things (IoBNT) applications is possible using Molecular Communication (MC), where two or multiple nodes communicate with each other by transmitting chemical molecules. The basic steps involved in MC are the transmission of molecules, propagation of molecules in the medium, and reception of the molecules at the receiver. Various transmission schemes, channel models, and detection techniques have been proposed for MC in recent years. This paper, therefore, presents an exhaustive review of the existing literature on detection techniques along with their transmission schemes under various MC setups. More specifically, for each setup, this survey includes the transmission and detection techniques under four different environments to support various IoBNT applications: (i) static transmitter and receiver in a pure-diffusive channel, (ii) static transmitter and receiver in a flow-induced diffusive channel, (iii) mobile transmitter and receiver in a pure-diffusive channel, (iv) mobile transmitter and receiver in a flow-induced diffusive channel. Also, performances and complexities of various detection schemes have been compared. Further, several challenges in detection and their possible solutions have been discussed under both static and mobile scenarios. Furthermore, some experimental works in MC are presented to show realistic transmission and detection procedures available in practice. Finally, future research directions and challenges in the practical design of the transmitter and receiver are described to realize MC for IoBNT health applications.

Review of the Characteristics of Food-Derived and Endogenous Nɛ-Carboxymethyllysine

2013 ◽  
Vol 76 (5) ◽  
pp. 912-918 ◽  
Author(s):  
LIPENG HAN ◽  
LIN LI ◽  
BING LI ◽  
DI ZHAO ◽  
YUTING LI ◽  
...  
Keyword(s):  
Human Health ◽  
Detection Methods ◽  
Future Research ◽  
Potential Hazard ◽  
Health Food ◽  
Research Directions ◽  
Glycation End Products ◽  
Future Research Directions ◽  
Different Characteristics ◽  
Food Processes

N ɛ-Carboxymethyllysine (CML), a representative of advanced glycation end products (AGEs), is commonly found in food and is considered a potential hazard to human health. Food scientists have begun to investigate the formation of CML in food processes. As the understanding of CML is mainly based on that of endogenous CML from the fields of biology and medicine, this review summarizes the different characteristics of food-derived CML and endogenous CML with respect to food safety, detection methods, formation environment, formation mechanism, and methods for inhibiting the formation of CML. Additionally, future research directions for the study of food-derived CML are proposed, including understanding its digestion, absorption, and metabolism in human health, developing rapid, reliable, and inexpensive detection methods, revealing its relationship with food components and production processes, and controlling the formation of CML through the addition of inhibitors and/or modification of food processing conditions, so as to contribute to the methods for controlling food-derived AGEs.

scholarly journals On the Cauchy Problem of Vectorial Thermostatted Kinetic Frameworks

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 517
Author(s):  
Carlo Bianca ◽  
Bruno Carbonaro ◽  
Marco Menale
Keyword(s):  
Cauchy Problem ◽  
Mathematical Analysis ◽  
Social Systems ◽  
Future Research ◽  
Research Directions ◽  
State Variable ◽  
The Cauchy Problem ◽  
Uniqueness Of The Solution ◽  
Global Existence And Uniqueness ◽  
Future Research Directions

This paper is devoted to the derivation and mathematical analysis of new thermostatted kinetic theory frameworks for the modeling of nonequilibrium complex systems composed by particles whose microscopic state includes a vectorial state variable. The mathematical analysis refers to the global existence and uniqueness of the solution of the related Cauchy problem. Specifically, the paper is divided in two parts. In the first part the thermostatted framework with a continuous vectorial variable is proposed and analyzed. The framework consists of a system of partial integro-differential equations with quadratic type nonlinearities. In the second part the thermostatted framework with a discrete vectorial variable is investigated. Real world applications, such as social systems and crowd dynamics, and future research directions are outlined in the paper.

scholarly journals Methods of Detecting Insufficient Effort Responding: Comparisons and Practical Recommendations

2019 ◽  
Vol 80 (2) ◽  
pp. 312-345 ◽  
Author(s):  
Maxwell Hong ◽  
Jeffrey T. Steedle ◽  
Ying Cheng
Keyword(s):  
Psychometric Properties ◽  
Detection Methods ◽  
Future Research ◽  
Validity And Reliability ◽  
Multiple Sources ◽  
Research Directions ◽  
Different Types ◽  
Future Research Directions ◽  
Biased Responding ◽  
Practical Recommendations

Insufficient effort responding (IER) affects many forms of assessment in both educational and psychological contexts. Much research has examined different types of IER, IER’s impact on the psychometric properties of test scores, and preprocessing procedures used to detect IER. However, there is a gap in the literature in terms of practical advice for applied researchers and psychometricians when evaluating multiple sources of IER evidence, including the best strategy or combination of strategies when preprocessing data. In this study, we demonstrate how the use of different IER detection methods may affect psychometric properties such as predictive validity and reliability. Moreover, we evaluate how different data cleansing procedures can detect different types of IER. We provide evidence via simulation studies and applied analysis using the ACT’s Engage assessment as a motivating example. Based on the findings of the study, we provide recommendations and future research directions for those who suspect their data may contain responses reflecting careless, random, or biased responding.

scholarly journals FRAME SIMULATIONS IN 18TH CENTURY AZULEJOS

ARTis ON ◽  
2016 ◽  
pp. 42-53
Author(s):  
Ana Raquel Machado ◽  
Rosário Salema de Carvalho
Keyword(s):  
Present Article ◽  
18Th Century ◽  
The Other ◽  
Future Research ◽  
Research Project ◽  
Research Directions ◽  
Ongoing Research ◽  
Other Hand ◽  
Future Research Directions ◽  
The One

The present article is the result of an ongoing research project and aims to draw attention to simulated azulejo frames. On the one hand, these decorative elements isolate the compositions, closing them in on themselves. On the other hand, simulated azulejo frames help integrate with the tile decoration they are part of, which in turn is part of a global decorative system in line with the concept of artistic totality typical of the Baroque period. This article will examine the various solutions that resort to this kind of frame, known as canvas-frame, including decorative elements, plastic compositions and their effects or consequences for the overall decorations. Finally, the focus will turn to future research directions, based on the systematic cataloguing of the known examples.

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Sign in / Sign up

Export Citation Format

Share Document