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Deepfake is a type of artificial intelligence created by combining the phrases deep learning and fake that facilitates in developing videos that have been fabricated via using deep learning techniques. It is a subset of AI that refers to algorithms that can learn and make intelligent judgments without human intervention. A deep-learning system can create convincing impersonations by examining images and videos of a target person from various perspectives and then copying their behavior and voice patterns. Once a prototype fake has been created, GANs (Generative Adversarial Networks) are used to make it more credible. The GANs method aims to find faults in the system and make adjustments to fix them.

How can you stay away from deepfake videos?

1. Deepfake videos are much simpler to spot than deepfake photographs. And you may accomplish so with the assistance of two factors. When a deepfake video of a person is created, for example, there is little difference between the person and the backdrop. However, you may spot a false video if the attention is solely on the face in the video and the surrounding is purposefully obscured.
2. Deepfake can be easily avoided by restricting personal images on social media and avoiding close-up photos of your face as much as possible.
3. Advanced artificial intelligence algorithms are under development which can swiftly identify deepfake videos thereby preventing people from falling prey to fake news and fake films.

When and where did deepfake start?

Deep Fake was a user on Reddit in 2017 who began employing face modification technology for pornography. It was from here that the term Deepfake was coined, and videos like this were known as Deepfake Videos. 

Deepfake as a boon to technology

MyHeritage, a software program, has been in the headlines for transforming any image into a 10-second movie. With this app, you may also breathe new life into old images using this program. With the use of this program, images of prominent personalities from past were transformed into movies. And these films show that if Artificial Intelligence is applied correctly, this approach may be beneficial to humans.

Deepfake as a threat to humanity

A.  Deepfakes were used to subvert democracy in the United States 

Facebook decided to prohibit the use of deep fakes after fake videos of politicians began spreading on social media. They allowed a few loopholes, such as the ability to keep sarcastic films and photos, but distinguishing between satire and agenda-driven content is difficult.

B.  Deepfakes began to be exploited by internet predators 

People began leveraging the ability to substitute anyone's face in an image or video to make pornographic content without their consent. As the deep fake technology allows them to do so by replacing face and expressions; all cybercriminals need is a profile photograph on social media to produce fake material to produce fake videos.

C.  Deepfake to tarnishing reputation of individuals 

A Pennsylvania mom, for example, was prosecuted for harassing cheerleaders at her daughter's school by employing deep fakes. The mother used manipulated recordings to carry out a cyberbullying campaign against girls she viewed as competitors to her daughter.

Deepfakes still continues pushing the digital media envelope where researchers suggest using NFTs (Non-Fungible Tokens) is the most effective strategy to combat deep fake. But NFTs, on the other hand, are still far away from being the standard on blockchains like Ethereum.

To know more about various cyber threats and methods to prevent them, contact Centex Technologies at (972) 375-9654.

Automation refers to the use of technology for performing tasks with reduced human assistance. It can be applied to any industry that involves repetitive tasks. However, it is more profoundly implemented in the industries of robotics, manufacturing, automotives and technology.

In the technology industry, automation is used for developing IT systems and business decision software.

• IT Automation: In case of IT, automation can be integrated with and applied to anything from network automation to infrastructure, methodologies, DevOps, cloud, edge computing, security, testing, monitoring, and alerting.
• Business Automation: It involves the alignment of business process management and business rules management with the process of modern application development. The underlying goal of business automation is to meet changing market demands.

The current market scenario requires businesses to undergo Digital Transformation. Instead of focusing on streamlining processes like automating customer records for sales, businesses now need to focus on developing new opportunities like automating complete business operations. This requires business and IT leaders to partner together for developing automation software and applications for business operations.

However, a simple question that needs to be answered is: Why Should a Business Adopt Automation Software?

In modern day scenario, businesses face multiple challenges such as supporting their employees, reaching out to new customers, providing innovative products & services at a faster speed. Automation software helps the business in managing, changing and adapting its IT infrastructure as well as business operations. Simplifying basic operational processes frees up time for businesses to focus on innovation and creativity.

Here are some other reasons that support the decision of adopting automation software for businesses:

• It is hard to manage IT operations and processes while adopting new processes and staying in compliance with dynamic legal systems.
• Requirements and demand are growing exponentially faster as compared to IT and business capabilities.
• New methodologies such as DevOps are forcing changes in business culture.
• The scaling up of business technology including virtualization, Cloud, etc. is too extensive to be performed manually.

An automation software for businesses holds its importance in improving productivity, consistency, and efficiency. Some advantages of automation software for businesses are:

• Higher Productivity: As the automation software handles the repetitive tasks, the IT team is free to use the skills for more productive tasks such as developing new opportunities.
• Better Reliability: Reducing the amount of human intervention in repetitive tasks helps in reducing the errors. A software brings reliability to the tasks as the processes, testing, updates, and workflow happen in the same order and time, making the results more reliable.
• Easier Governance: A software can be coded easily to implement any changes making it easier to oversee the implementation and processes.

For more information on automation software, call Centex Technologies at (972) 375 - 9654.      

Artificial Intelligence (AI), Machine Learning and Deep Learning are commonly used interchangeably. However, in technological context; Machine Learning and Deep Learning are subsets of AI. In order to understand the difference between these terms, it is important to know the actual meaning of individual term.

Artificial Intelligence (AI): Artificial Intelligence is a term that defines the simulation of human intelligence processes by computer systems. The processes include learning, reasoning and self-correction. AI is broadly classified as weak (narrow) AI and strong AI. Weak AI systems are designed to do a particular task. The most common example of weak AI is the virtual personal assistants. On the contrary, strong AI systems are equipped with generalized human cognitive abilities. These systems are able to find a solution to any problem independent of human intervention.

Machine Learning (ML): ML is an application or subset of Artificial Intelligence. Under this application of AI, a machine is programmed to access and manipulate data. The machine can analyze the data to identify patterns and learn from these patterns. This allows the machine or computer system to modify decisions as per any change in data without explicit programming. Machine Learning is driven by algorithms and stat models. The common usage of Machine Learning can be found in apps such as email filtering, optimization, internet fraud detection, etc. Machine Learning methods are widely grouped as supervised and unsupervised ML.

• Unsupervised ML: These methods group interpret data based only on input data. Clustering methods are an example of unsupervised ML.
• Supervised ML: Supervised ML methods use both input and output data to develop a predictive model. Classification and Regression methods are listed as supervised ML.

Deep Learning (DL): It is a broader subset of AI. Deep Learning involves collection of large unstructured data and combing through it to generate classified structured information. The basic difference between Machine Learning and Deep Learning is that ML is task oriented learning, whereas DL is more general. It is used to derive meaning or identify patterns in unstructured data. This, in turn, helps in spotting large scale trends or irregularities. Some common applications of Deep Learning include self-driving cars, fraud news detection, natural language processing, visual recognition, etc.

For more information about Artificial Intelligence, Machine Learning and Deep Learning, call Centex Technologies at (972) 375-9654.