A Novel Extended Ripple and Cyberbullies Data Detection (E- RACYBDD) Framework to Mitigate Deep Fake Attacks on Social Media

2021 ◽  
pp. 186-205
Author(s):  
Bhimavarapu Usharani
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Social Media ◽  
Digital Image ◽  
Generative Adversarial Networks ◽  
Data Detection ◽  
Adversarial Networks ◽  
Video And Audio ◽  
Present World ◽  
New Framework

Social media is one of the topmost communications in the present world and some discomforts are also hidden in this. Social media is one of the platforms for cyberbullying. Teenagers who are not under the guidance of their parents are the major victims of cyberstalking and cyber abuses. Forensic image processing enhances the digital image using different computer techniques. Deep fake uses the technology to superimpose more than one image, video, and audio with other images, videos, and audios with the well-known artificial intelligence and generative adversarial networks (GANs). This technology raises the curtain to create many fake images, fake videos, especially porn videos and fake audios of celebrities, especially actors and politicians. This chapter explains the impacts of deep fakes in social media, especially Facebook and Twitter. This chapter proposed a new framework called the extended-RACYBDD to detect the deep attacks.

Quantum generative adversarial networks for learning and loading quantum image in noisy environment

2021 ◽  
pp. 2150360
Author(s):  
Wanghao Ren ◽  
Zhiming Li ◽  
Yiming Huang ◽  
Runqiu Guo ◽  
Lansheng Feng ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantum Circuit ◽  
Quantum Image Processing ◽  
Quantum Circuits ◽  
Generative Adversarial Networks ◽  
Channel Noise ◽  
Quantum Image ◽  
Adversarial Networks ◽  
Potential Applications ◽  
Classical Image

Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend quantum generative adversarial networks to the quantum image processing field and show how to learning and loading an classical image using quantum circuits. By reducing quantum gates without gradient changes, we reduced the number of basic quantum building block from 15 to 13. Our framework effectively generates pure state subject to bit flip, bit phase flip, phase flip, and depolarizing channel noise. We numerically simulate the loading and learning of classical images on the MINST database and CIFAR-10 database. In the quantum image processing field, our framework can be used to learn a quantum image as a subroutine of other quantum circuits. Through numerical simulation, our method can still quickly converge under the influence of a variety of noises.

The Frontiers of Deep Learning for Earth System Modelling 

2021 ◽  
Author(s):  
David Hall
Keyword(s):  
Artificial Intelligence ◽  
Bias Correction ◽  
Neural Nets ◽  
Generative Adversarial Networks ◽  
System Modelling ◽  
Earth System ◽  
Adversarial Networks ◽  
The Earth ◽  
Medium Range ◽  
Earth System Modelling

<p>This talk gives an overview of cutting-edge artificial intelligence applications and techniques for the earth-system sciences. We survey the most important recent contributions in areas including extreme weather, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias-correction, generative adversarial networks, data in-painting, network-HPC coupling, physics-informed neural nets, and geoengineering, amongst others. Then, we describe recent AI breakthroughs that have the potential to be of greatest benefit to the geosciences. We also discuss major open challenges in AI for science and their potential solutions. This talk is a living document, in that it is updated frequently, in order to accurately relect this rapidly changing field.</p>

scholarly journals Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow

2021 ◽  
pp. 3-12
Author(s):  
Jean Jaminet ◽  
Gabriel Esquivel ◽  
Shane Bugni
Keyword(s):  
Artificial Intelligence ◽  
Data Storage ◽  
Intelligent Systems ◽  
Communication Theory ◽  
Human Perception ◽  
Generative Adversarial Networks ◽  
Virtual Design ◽  
Storage And Retrieval ◽  
Adversarial Networks ◽  
Design Production

AbstractVirtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.

scholarly journals Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Holly Burrows ◽  
Javad Zarrin ◽  
Lakshmi Babu-Saheer ◽  
Mahdi Maktab-Dar-Oghaz
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
User Interface ◽  
Convolutional Neural Networks ◽  
Mental Health Problems ◽  
Well Being ◽  
Working Environment ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells

2020 ◽  
Vol 24 (1) ◽  
pp. 134-141 ◽  
Author(s):  
Saori Aida ◽  
Hiroyuki Kameda ◽  
Sakae Nishisako ◽  
Tomonari Kasai ◽  
Atsushi Sato ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Stem Cells ◽  
Drug Discovery ◽  
Cancer Stem Cells ◽  
Low Cost ◽  
Generative Adversarial Networks ◽  
Cell Detection ◽  
Smart System ◽  
Adversarial Networks ◽  
Normal Image

The realization of effective and low-cost drug discovery is imperative to enable people to easily purchase and use medicines when necessary. This paper reports a smart system for detecting iPSC-derived cancer stem cells by using conditional generative adversarial networks. This system with artificial intelligence (AI) accepts a normal image from a microscope and transforms it into a corresponding fluorescent-marked fake image. The AI system learns 10,221 sets of paired pictures as input. Consequently, the system’s performance shows that the correlation between true fluorescent-marked images and fake fluorescent-marked images is at most 0.80. This suggests the fundamental validity and feasibility of our proposed system. Moreover, this research opens a new way for AI-based drug discovery in the process of iPSC-derived cancer stem cell detection.

scholarly journals De Novo Drug Design using Artificial Intelligence ASYNT-GAN

2020 ◽  
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
De Novo ◽  
Protein Structures ◽  
Generative Adversarial Networks ◽  
Text Generation ◽  
De Novo Drug Design ◽  
3D Space ◽  
Adversarial Networks

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

scholarly journals De Novo Drug Design using Artificial Intelligence ASYNT-GAN

2020 ◽  
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
De Novo ◽  
Protein Structures ◽  
Generative Adversarial Networks ◽  
Text Generation ◽  
De Novo Drug Design ◽  
3D Space ◽  
Adversarial Networks

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

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